Udacity - Machine Learning Engineer Nanodegree nd009t v1.0.0 - TorrentBank

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Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt 104 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.pt-BR.vtt 105 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt 107 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt 108 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.pt-BR.vtt 109 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.en.vtt 109 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt 113 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt 118 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.ar.vtt 122 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.pt-BR.vtt 124 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.zh-CN.vtt 125 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.zh-CN.vtt 125 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.en.vtt 138 B
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.en.vtt 139 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.ar.vtt 140 B
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.en.vtt 140 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.en.vtt 141 B
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.pt-BR.vtt 141 B
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.pt-BR.vtt 143 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.pt-BR.vtt 164 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.en.vtt 164 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.zh-CN.vtt 165 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.zh-CN.vtt 166 B
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.zh-CN.vtt 166 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.zh-CN.vtt 167 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.ar.vtt 168 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.ar.vtt 171 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.pt-BR.vtt 171 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.pt-BR.vtt 180 B
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.pt-BR.vtt 186 B
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.ar.vtt 203 B
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.ar.vtt 204 B
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt 204 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.en.vtt 205 B
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt 206 B
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.en.vtt 207 B
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.en.vtt 208 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.en.vtt 214 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.zh-CN.vtt 222 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.pt-BR.vtt 226 B
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.ar.vtt 226 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.en.vtt 229 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.pt-BR.vtt 230 B
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.zh-CN.vtt 232 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.pt-BR.vtt 233 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.zh-CN.vtt 243 B
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.zh-CN.vtt 245 B
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.ar.vtt 258 B
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.pt-BR.vtt 271 B
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.en.vtt 273 B
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt 277 B
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.zh-CN.vtt 277 B
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.ar.vtt 282 B
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.ar.vtt 284 B
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.pt-BR.vtt 292 B
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.en.vtt 292 B
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.en.vtt 298 B
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.zh-CN.vtt 299 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.ar.vtt 301 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt 301 B
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.pt-BR.vtt 302 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en.vtt 303 B
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.zh-CN.vtt 305 B
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.ar.vtt 306 B
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt 306 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt 309 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.en.vtt 312 B
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt 315 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.zh-CN.vtt 316 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.en.vtt 320 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt 324 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.en.vtt 325 B
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.en.vtt 325 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.pt-BR.vtt 326 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt 326 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.pt-BR.vtt 331 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt 332 B
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.zh-CN.vtt 335 B
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.zh-CN.vtt 342 B
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt 355 B
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt 355 B
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.ar.vtt 357 B
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.ar.vtt 359 B
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.ar.vtt 360 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.zh-CN.vtt 361 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt 361 B
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.pt-BR.vtt 362 B
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 B
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.en.vtt 368 B
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt 369 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.zh-CN.vtt 369 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt 370 B
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt 385 B
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt 385 B
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 B
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 B
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.ar.vtt 393 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.en.vtt 395 B
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.zh-CN.vtt 396 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.en.vtt 399 B
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.pt-BR.vtt 402 B
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.en.vtt 406 B
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.zh-CN.vtt 408 B
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.pt-BR.vtt 410 B
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.en.vtt 418 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 B
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.en.vtt 419 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.zh-CN.vtt 419 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.pt-BR.vtt 420 B
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 B
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 B
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.pt-BR.vtt 421 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.zh-CN.vtt 422 B
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.pt-BR.vtt 423 B
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.zh-CN.vtt 424 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.ar.vtt 425 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.zh-CN.vtt 425 B
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.ar.vtt 425 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.pt-BR.vtt 426 B
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt 432 B
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 B
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt 437 B
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt 439 B
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.zh-CN.vtt 440 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.ar.vtt 444 B
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt 451 B
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 B
README.txt 454 B
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt 454 B
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.pt-BR.vtt 454 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.zh-CN.vtt 456 B
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.en.vtt 457 B
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt 457 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.en.vtt 458 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 B
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt 465 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt 466 B
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt 467 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt 468 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt 472 B
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.en.vtt 472 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.en.vtt 473 B
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt 473 B
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.pt-BR.vtt 474 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt 475 B
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.en.vtt 476 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt 477 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 B
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.ar.vtt 479 B
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.pt-BR.vtt 482 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.en.vtt 483 B
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.zh-CN.vtt 485 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.zh-CN.vtt 487 B
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt 488 B
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.zh-CN.vtt 488 B
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.en.vtt 489 B
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt 490 B
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.ar.vtt 490 B
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.pt-BR.vtt 497 B
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.zh-CN.vtt 498 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.zh-CN.vtt 499 B
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.en.vtt 501 B
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 B
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.ar.vtt 505 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt 505 B
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.zh-CN.vtt 507 B
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.pt-BR.vtt 507 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt 508 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt 510 B
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.ar.vtt 510 B
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.ar.vtt 512 B
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.pt-BR.vtt 512 B
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.en.vtt 514 B
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt 514 B
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt 517 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.pt-BR.vtt 518 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt 521 B
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt 524 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt 526 B
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt 530 B
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt 533 B
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.zh-CN.vtt 535 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt 538 B
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 B
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt 540 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt 540 B
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.en.vtt 540 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 B
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt 543 B
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 B
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 B
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.pt-BR.vtt 549 B
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 B
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt 555 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 B
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt 557 B
Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt 558 B
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.ar.vtt 559 B
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.zh-CN.vtt 560 B
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.zh-CN.vtt 561 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.ar.vtt 561 B
Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.zh-CN.vtt 568 B
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.ar.vtt 570 B
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.pt-BR.vtt 573 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.en.vtt 573 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt 574 B
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.en.vtt 579 B
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.zh-CN.vtt 580 B
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt 583 B
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.zh-CN.vtt 584 B
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt 586 B
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt 588 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt 589 B
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.zh-CN.vtt 590 B
Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt 590 B
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.pt-BR.vtt 592 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.zh-CN.vtt 593 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt 594 B
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt 595 B
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.en.vtt 596 B
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.ar.vtt 597 B
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt 599 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.pt-BR.vtt 599 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 B
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.en.vtt 600 B
Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.en.vtt 601 B
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt 601 B
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt 606 B
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.en.vtt 607 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt 607 B
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 B
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 B
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.en-US.vtt 608 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.zh-CN.vtt 612 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.en.vtt 613 B
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt 615 B
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt 618 B
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.en.vtt 622 B
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.ar.vtt 624 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 B
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt 631 B
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt 633 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 B
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt 635 B
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt 638 B
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.en-US.vtt 638 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt 643 B
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt 643 B
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.en.vtt 644 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt 655 B
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.pt-BR.vtt 655 B
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt.vtt 656 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt 657 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.zh-CN.vtt 662 B
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt 663 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt 663 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt 663 B
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.en.vtt 665 B
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.zh-CN.vtt 668 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.pt-BR.vtt 671 B
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt 672 B
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt 675 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.zh-CN.vtt 677 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.pt-BR.vtt 678 B
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.zh-CN.vtt 680 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.ar.vtt 682 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.pt-BR.vtt 683 B
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.pt-BR.vtt 683 B
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.en.vtt 685 B
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en.vtt 688 B
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.pt-BR.vtt 688 B
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt 690 B
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.zh-CN.vtt 692 B
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.ar.vtt 694 B
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.en.vtt 694 B
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.pt-BR.vtt 694 B
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.ar.vtt 697 B
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt 701 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.zh-CN.vtt 701 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.en.vtt 702 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt 705 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.en.vtt 707 B
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.pt-BR.vtt 707 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt 707 B
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.es-MX.vtt 707 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt 709 B
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.ar.vtt 711 B
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt 716 B
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en-US.vtt 716 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.pt-BR.vtt 716 B
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.zh-CN.vtt 718 B
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 B
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.zh-CN.vtt 720 B
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt 723 B
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.zh-CN.vtt 723 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.en.vtt 725 B
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt 727 B
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.pt-BR.vtt 727 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt 729 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.pt-BR.vtt 730 B
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.zh-CN.vtt 733 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.zh-CN.vtt 734 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.pt-BR.vtt 736 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.pt-BR.vtt 737 B
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 B
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.en.vtt 739 B
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.zh-CN.vtt 742 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.zh-CN.vtt 744 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.ar.vtt 745 B
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.en.vtt 747 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.pt-BR.vtt 754 B
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.zh-CN.vtt 756 B
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.pt-BR.vtt 760 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.en-US.vtt 764 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.zh-CN.vtt 766 B
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en.vtt 767 B
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.en.vtt 768 B
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.ar.vtt 769 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt 769 B
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt 769 B
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en-US.vtt 770 B
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt 771 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.pt-BR.vtt 772 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.en.vtt 772 B
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.pt-BR.vtt 773 B
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.en.vtt 775 B
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.en.vtt 777 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt 777 B
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.pt-BR.vtt 781 B
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.ar.vtt 784 B
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt 786 B
Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.zh-CN.vtt 787 B
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 B
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.en.vtt 791 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt 791 B
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt 793 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt 793 B
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.en.vtt 797 B
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.zh-CN.vtt 801 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.en.vtt 804 B
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 B
Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt 804 B
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.zh-CN.vtt 806 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.zh-CN.vtt 810 B
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.zh-CN.vtt 810 B
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt 812 B
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.zh-CN.vtt 812 B
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 B
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.zh-CN.vtt 814 B
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.pt-BR.vtt 817 B
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.ar.vtt 820 B
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.en.vtt 820 B
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.zh-CN.vtt 822 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.zh-CN.vtt 822 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt 823 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.zh-CN.vtt 823 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.en.vtt 824 B
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.pt-BR.vtt 826 B
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.en.vtt 828 B
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.en.vtt 830 B
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt 831 B
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.es-MX.vtt 832 B
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.ar.vtt 836 B
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt 840 B
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.ar.vtt 841 B
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.ar.vtt 842 B
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt 842 B
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt 845 B
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.zh-CN.vtt 849 B
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.en.vtt 850 B
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.pt-BR.vtt 853 B
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Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.en.vtt 855 B
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.pt-BR.vtt 856 B
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Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.pt-BR.vtt 893 B
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Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.en.vtt 910 B
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Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.en.vtt 937 B
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Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt 1.00 KB
Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.pt-BR.vtt 1.00 KB
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Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.en.vtt 1.00 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.zh-CN.vtt 1.00 KB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.en.vtt 1.00 KB
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Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.zh-CN.vtt 1.01 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.zh-CN.vtt 1.01 KB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.zh-CN.vtt 1.01 KB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.pt-BR.vtt 1.02 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.zh-CN.vtt 1.02 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.pt-BR.vtt 1.02 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.pt-BR.vtt 1.02 KB
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Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.zh-CN.vtt 1.02 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.zh-CN.vtt 1.02 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.pt-BR.vtt 1.02 KB
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Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.ar.vtt 1.04 KB
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Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.pt-BR.vtt 1.05 KB
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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt 1.07 KB
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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.pt-BR.vtt 1.08 KB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.zh-CN.vtt 1.08 KB
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Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.pt-BR.vtt 1.09 KB
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.pt-BR.vtt 1.09 KB
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Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.pt-BR.vtt 1.09 KB
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Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.ar.vtt 1.10 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.en.vtt 1.11 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.en.vtt 1.11 KB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.zh-CN.vtt 1.11 KB
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Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt 1.15 KB
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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.zh-CN.vtt 1.17 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.pt-BR.vtt 1.18 KB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.en.vtt 1.18 KB
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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.ar.vtt 1.35 KB
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Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.zh-CN.vtt 1.37 KB
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Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt 1.39 KB
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Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.en.vtt 1.39 KB
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Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.zh-CN.vtt 1.41 KB
Part 03-Module 01-Lesson 01_Linear Regression/img/y.gif 1.41 KB
Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt 1.41 KB
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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.zh-CN.vtt 1.42 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.zh-CN.vtt 1.42 KB
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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.en.vtt 1.42 KB
Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt 1.42 KB
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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt 1.43 KB
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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.en.vtt 1.44 KB
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Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt 1.46 KB
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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt 1.47 KB
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Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.en.vtt 1.50 KB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en-US.vtt 1.50 KB
Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt 1.50 KB
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Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt 1.78 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.pt-BR.vtt 1.78 KB
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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.81 KB
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Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.en.vtt 1.83 KB
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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.en.vtt 2.06 KB
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Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.zh-CN.vtt 2.11 KB
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Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.zh-CN.vtt 2.12 KB
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Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.zh-CN.vtt 2.14 KB
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Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt 2.16 KB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt 2.16 KB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.ar.vtt 2.16 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.17 KB
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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.17 KB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.zh-CN.vtt 2.17 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt 2.17 KB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.en.vtt 2.17 KB
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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.pt-BR.vtt 2.19 KB
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Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.zh-CN.vtt 2.20 KB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.en.vtt 2.20 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.zh-CN.vtt 2.20 KB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt 2.21 KB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt 2.21 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.en.vtt 2.21 KB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.en.vtt 2.21 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt 2.22 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.zh-CN.vtt 2.22 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.zh-CN.vtt 2.22 KB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en.vtt 2.22 KB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt 2.22 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.pt-BR.vtt 2.22 KB
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Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.zh-CN.vtt 2.22 KB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.zh-CN.vtt 2.22 KB
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Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.zh-CN.vtt 2.23 KB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt 2.23 KB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.zh-CN.vtt 2.24 KB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt 2.24 KB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.pt-BR.vtt 2.24 KB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.zh-CN.vtt 2.24 KB
Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt 2.24 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.es-MX.vtt 2.24 KB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.pt-BR.vtt 2.25 KB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.zh-CN.vtt 2.26 KB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt 2.26 KB
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Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.zh-CN.vtt 2.27 KB
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Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.ar.vtt 2.28 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.zh-CN.vtt 2.28 KB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.ar.vtt 2.28 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.pt-BR.vtt 2.28 KB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt 2.28 KB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt 2.28 KB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.en.vtt 2.28 KB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.pt-BR.vtt 2.28 KB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt 2.28 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt 2.29 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.en.vtt 2.29 KB
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Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en.vtt 2.29 KB
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Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt 2.30 KB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en-US.vtt 2.30 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt 2.30 KB
Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.en.vtt 2.30 KB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en.vtt 2.30 KB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.zh-CN.vtt 2.30 KB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt 2.30 KB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.ar.vtt 2.30 KB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en-US.vtt 2.30 KB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.ar.vtt 2.30 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.zh-CN.vtt 2.31 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.zh-CN.vtt 2.31 KB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.en.vtt 2.31 KB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt 2.31 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.31 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.31 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.31 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.en.vtt 2.32 KB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt 2.32 KB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt 2.33 KB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.pt-BR.vtt 2.33 KB
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Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt 2.36 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.36 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.36 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.en.vtt 2.36 KB
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Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.zh-CN.vtt 2.37 KB
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Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en-US.vtt 2.37 KB
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Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt 2.38 KB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt 2.38 KB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.pt-BR.vtt 2.39 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt 2.39 KB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.pt-BR.vtt 2.40 KB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt 2.40 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.40 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.40 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.40 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.pt-BR.vtt 2.41 KB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.en.vtt 2.41 KB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt 2.41 KB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.en.vtt 2.41 KB
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Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.pt-BR.vtt 2.41 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt 2.41 KB
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Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt 2.42 KB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt 2.43 KB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.pt-BR.vtt 2.43 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.pt-BR.vtt 2.43 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.en.vtt 2.44 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.es-MX.vtt 2.44 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt 2.45 KB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.zh-CN.vtt 2.45 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.en.vtt 2.45 KB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en.vtt 2.46 KB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt 2.46 KB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en-US.vtt 2.46 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt 2.47 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt 2.47 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.zh-CN.vtt 2.48 KB
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Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.pt-BR.vtt 2.48 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.48 KB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.en.vtt 2.48 KB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en.vtt 2.48 KB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt 2.48 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.pt-BR.vtt 2.48 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.en.vtt 2.48 KB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en-US.vtt 2.49 KB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.en.vtt 2.49 KB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.pt-BR.vtt 2.49 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt 2.49 KB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.pt-BR.vtt 2.50 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.en.vtt 2.50 KB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.zh-CN.vtt 2.50 KB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.pt-BR.vtt 2.50 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en.vtt 2.50 KB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.en.vtt 2.50 KB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.pt-BR.vtt 2.50 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en-US.vtt 2.50 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.zh-CN.vtt 2.50 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.zh-CN.vtt 2.50 KB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.en.vtt 2.50 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt 2.51 KB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.zh-CN.vtt 2.51 KB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt 2.51 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt 2.51 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en.vtt 2.51 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.pt-BR.vtt 2.51 KB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.pt-BR.vtt 2.51 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en-US.vtt 2.52 KB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en.vtt 2.52 KB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt 2.52 KB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en-US.vtt 2.52 KB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.zh-CN.vtt 2.53 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.53 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.53 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.53 KB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.zh-CN.vtt 2.54 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.54 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.54 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.54 KB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en.vtt 2.54 KB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt 2.55 KB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en-US.vtt 2.55 KB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.zh-CN.vtt 2.55 KB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.pt-BR.vtt 2.55 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt 2.55 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.en.vtt 2.56 KB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.zh-CN.vtt 2.56 KB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.pt-BR.vtt 2.56 KB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt 2.56 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt 2.57 KB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.pt-BR.vtt 2.57 KB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.zh-CN.vtt 2.57 KB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.zh-CN.vtt 2.58 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.58 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.58 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.58 KB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.en.vtt 2.58 KB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.en-US.vtt 2.58 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.zh-CN.vtt 2.58 KB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.en.vtt 2.59 KB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.es-MX.vtt 2.59 KB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt 2.59 KB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.ar.vtt 2.61 KB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.pt-BR.vtt 2.61 KB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.zh-CN.vtt 2.61 KB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.es-MX.vtt 2.63 KB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.pt-BR.vtt 2.63 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.pt-BR.vtt 2.64 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.en-US.vtt 2.64 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.en.vtt 2.64 KB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt 2.64 KB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.en.vtt 2.64 KB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt 2.64 KB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.en.vtt 2.64 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.pt-BR.vtt 2.64 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.en.vtt 2.65 KB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.en.vtt 2.65 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.en.vtt 2.65 KB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.en-US.vtt 2.65 KB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.zh-CN.vtt 2.65 KB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.en.vtt 2.65 KB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.en.vtt 2.65 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.pt-BR.vtt 2.65 KB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.pt-BR.vtt 2.65 KB
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Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt 2.66 KB
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Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.en.vtt 2.66 KB
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Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.pt-BR.vtt 2.66 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.pt-BR.vtt 2.67 KB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.zh-CN.vtt 2.67 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.pt-BR.vtt 2.67 KB
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Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.en.vtt 2.68 KB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.en-US.vtt 2.68 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.en.vtt 2.68 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.en.vtt 2.69 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.pt-BR.vtt 2.70 KB
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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.pt-BR.vtt 2.70 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.en.vtt 2.70 KB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.en.vtt 2.70 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.en.vtt 2.71 KB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.es-MX.vtt 2.71 KB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt 2.72 KB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.en.vtt 2.72 KB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.zh-CN.vtt 2.72 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.zh-CN.vtt 2.72 KB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.ar.vtt 2.73 KB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.zh-CN.vtt 2.73 KB
Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.en.vtt 2.73 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.zh-CN.vtt 2.74 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt 2.74 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.74 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.74 KB
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Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.zh-CN.vtt 2.90 KB
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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.en.vtt 2.91 KB
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Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.zh-CN.vtt 2.91 KB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt 2.92 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.pt-BR.vtt 2.92 KB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.en.vtt 2.93 KB
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Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.pt-BR.vtt 2.94 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.en.vtt 2.94 KB
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Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt 2.95 KB
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Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.pt-BR.vtt 2.96 KB
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Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.en.vtt 2.97 KB
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Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.en.vtt 2.98 KB
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Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.ar.vtt 2.99 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt 2.99 KB
Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.pt-BR.vtt 3.00 KB
Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.zh-CN.vtt 3.00 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.en.vtt 3.00 KB
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Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt 3.00 KB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt 3.01 KB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.zh-CN.vtt 3.02 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt 3.02 KB
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Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.en-US.vtt 3.03 KB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.ar.vtt 3.03 KB
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Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.pt-BR.vtt 3.03 KB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.pt-BR.vtt 3.03 KB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt 3.03 KB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.zh-CN.vtt 3.04 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.zh-CN.vtt 3.04 KB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.zh-CN.vtt 3.04 KB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.pt-BR.vtt 3.04 KB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.en.vtt 3.04 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.en.vtt 3.04 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.zh-CN.vtt 3.04 KB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.zh-CN.vtt 3.05 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.en.vtt 3.05 KB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.ar.vtt 3.05 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.en.vtt 3.06 KB
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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.en.vtt 3.07 KB
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Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.en.vtt 3.11 KB
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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.en.vtt 3.12 KB
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Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.en.vtt 3.22 KB
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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.26 KB
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Part 02-Module 03-Lesson 01_Model Selection/12. Summary.html 5.79 KB
Part 10-Module 02-Lesson 06_Graphs/10. DFS.html 5.80 KB
Part 10-Module 02-Lesson 06_Graphs/11. BFS.html 5.80 KB
Part 04-Module 02-Lesson 02_Clustering Mini-Project/01. Intro.html 5.80 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies.html 5.80 KB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt 5.81 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.en-US.vtt 5.81 KB
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Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.pt-BR.vtt 5.81 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality.html 5.82 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/01. Introduction.html 5.82 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Intro to Deep Neural Networks.html 5.82 KB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources.html 5.82 KB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/05. Project Workspace.html 5.83 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up.html 5.84 KB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.pt-BR.vtt 5.84 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.pt-BR.vtt 5.84 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.en.vtt 5.85 KB
Part 04-Module 06-Lesson 01_Random Projection and ICA/05. FastICA Algorithm.html 5.85 KB
Part 04-Module 08-Lesson 01_Creating Customer Segments/06. Workspace.html 5.85 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies.html 5.86 KB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris.html 5.86 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example.html 5.86 KB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity.html 5.87 KB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/08. Experience.html 5.87 KB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path.html 5.88 KB
Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt 5.89 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt 5.89 KB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal.html 5.89 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure.html 5.89 KB
Part 03-Module 01-Lesson 08_Supervised Learning Project/06. Project Workspace.html 5.89 KB
Part 05-Module 01-Lesson 02_Cloud Computing/02. Create an AWS Account.html 5.89 KB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph.html 5.89 KB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys.html 5.89 KB
Part 02-Module 03-Lesson 01_Model Selection/13. Outro.html 5.89 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection.html 5.90 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure.html 5.91 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection.html 5.91 KB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices.html 5.92 KB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction.html 5.92 KB
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Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization.html 5.93 KB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles.html 5.94 KB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations.html 5.94 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion Matrix 2.html 5.94 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2.html 5.94 KB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt 5.95 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning.html 5.95 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary.html 5.95 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2.html 5.96 KB
Part 02-Module 03-Lesson 01_Model Selection/01. Types of Errors.html 5.97 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.en.vtt 5.97 KB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.zh-CN.vtt 5.97 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.en-US.vtt 5.97 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/index.html 5.97 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components.html 5.98 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning.html 5.98 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding.html 5.98 KB
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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely.html 5.99 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure.html 5.99 KB
Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.en.vtt 5.99 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2.html 5.99 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.pt-BR.vtt 5.99 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components.html 5.99 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.pt-BR.vtt 5.99 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions.html 5.99 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding.html 5.99 KB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network.html 5.99 KB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.pt-BR.vtt 6.00 KB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.en.vtt 6.00 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection.html 6.00 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization.html 6.00 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely.html 6.00 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions.html 6.01 KB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.zh-CN.vtt 6.02 KB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.zh-CN.vtt 6.02 KB
Part 11-Module 01-Lesson 01_Software and Tools/01. TensorFlow.html 6.03 KB
Part 01-Module 02-Lesson 01_Career Services Available to You/02. Access Your Career Portal.html 6.03 KB
Part 02-Module 03-Lesson 01_Model Selection/03. Cross Validation.html 6.03 KB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.zh-CN.vtt 6.05 KB
Part 10-Module 01-Lesson 05_Interview Practice/10. Arpan's Analysis of the Interview.html 6.05 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network.html 6.05 KB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/02. Quadcopter workspace.html 6.05 KB
Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.zh-CN.vtt 6.05 KB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction.html 6.06 KB
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Part 10-Module 01-Lesson 05_Interview Practice/01. Introduction.html 6.07 KB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.en.vtt 6.07 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Precision and Recall.html 6.07 KB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt 6.07 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components.html 6.07 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-equation-2.gif 6.08 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.pt-BR.vtt 6.08 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely.html 6.08 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction.html 6.08 KB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.25.34-pm.png 6.09 KB
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Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt 6.10 KB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. Welcome to the Machine Learning Engineer Nanodegree Program.html 6.10 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation.html 6.10 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion.html 6.11 KB
Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves.html 6.11 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces.html 6.11 KB
Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team.html 6.11 KB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.zh-CN.vtt 6.11 KB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt 6.11 KB
Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.en-US.vtt 6.11 KB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.en.vtt 6.11 KB
Part 04-Module 06-Lesson 01_Random Projection and ICA/08. [Lab] Independent Component Analysis.html 6.11 KB
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Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort.html 6.11 KB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt 6.12 KB
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Part 04-Module 06-Lesson 01_Random Projection and ICA/09. [Solution] Independent Component Analysis.html 6.12 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation.html 6.13 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets.html 6.13 KB
Part 03-Module 01-Lesson 04_Naive Bayes/01. Intro.html 6.14 KB
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Part 03-Module 01-Lesson 04_Naive Bayes/16. Outro.html 6.14 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning.html 6.15 KB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro.html 6.15 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt 6.16 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting.html 6.16 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt 6.17 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning.html 6.19 KB
Part 03-Module 01-Lesson 04_Naive Bayes/05. Bayes Theorem.html 6.19 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network.html 6.20 KB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro.html 6.21 KB
Part 01-Module 01-Lesson 03_Introductory Practice Project/02. Software Requirements.html 6.21 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.pt-BR.vtt 6.21 KB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/01. Project Overview.html 6.21 KB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/06. Skills.html 6.21 KB
Part 03-Module 01-Lesson 04_Naive Bayes/02. Guess the Person.html 6.21 KB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn.html 6.22 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort.html 6.22 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort.html 6.22 KB
Part 03-Module 01-Lesson 04_Naive Bayes/03. Known and Inferred.html 6.22 KB
Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search.html 6.22 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences.html 6.23 KB
Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.pt-BR.vtt 6.23 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay.html 6.23 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0).html 6.23 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/18. Outro.html 6.23 KB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0).html 6.23 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company.html 6.23 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort.html 6.23 KB
Part 03-Module 01-Lesson 04_Naive Bayes/07. Solution False Positives.html 6.23 KB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt 6.23 KB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/Project Description - Teach a Quadcopter How to Fly.html 6.24 KB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences.html 6.24 KB
Part 01-Module 01-Lesson 03_Introductory Practice Project/03. Project files.html 6.24 KB
Part 03-Module 01-Lesson 04_Naive Bayes/09. Bayesian Learning 2.html 6.24 KB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.en.vtt 6.25 KB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search.html 6.25 KB
Part 03-Module 01-Lesson 08_Supervised Learning Project/01. Overview.html 6.25 KB
Part 04-Module 04-Lesson 01_PCA/index.html 6.25 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure.html 6.26 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace.html 6.26 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding.html 6.26 KB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation.html 6.26 KB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.en.vtt 6.26 KB
Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.zh-CN.vtt 6.27 KB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt 6.27 KB
Part 09-Module 01-Lesson 02_LinkedIn Review/02. Resources in Your Career Portal.html 6.27 KB
Part 03-Module 01-Lesson 04_Naive Bayes/12. Naive Bayes Algorithm 2.html 6.27 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging.html 6.28 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries.html 6.29 KB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.en.vtt 6.29 KB
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Part 10-Module 02-Lesson 05_Trees/13. BST Complications.html 6.30 KB
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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative.html 6.31 KB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/08. Exercise Tile Coding.html 6.31 KB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/03. Analyzing Behavioral Answers.html 6.31 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming.html 6.31 KB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.ar.vtt 6.32 KB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!.html 6.32 KB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences.html 6.32 KB
Part 08-Module 01-Lesson 01_Conduct a Job Search/05. Resources in Your Career Portal.html 6.32 KB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt 6.32 KB
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Part 04-Module 06-Lesson 01_Random Projection and ICA/04. Independent Component Analysis (ICA).html 6.33 KB
Part 09-Module 01-Lesson 02_LinkedIn Review/Project Description - LinkedIn Profile Review Project.html 6.33 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. RBF Kernel 3.html 6.33 KB
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Part 03-Module 01-Lesson 05_Support Vector Machines/07. Margin Error.html 6.33 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Intro.html 6.33 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis.html 6.34 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/09. Error Function.html 6.34 KB
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Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales.html 6.34 KB
Part 06-Module 02-Lesson 02_Deep Q-Learning/12. TensorFlow Implementation.html 6.34 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs.html 6.35 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. The C Parameter.html 6.35 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks.html 6.35 KB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/05. Submitting the Project.html 6.35 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. Error Function Intuition.html 6.36 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis.html 6.37 KB
Part 02-Module 03-Lesson 01_Model Selection/10. Grid Search Lab.html 6.37 KB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/07. Program Readiness.html 6.37 KB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. Polynomial Kernel 1.html 6.37 KB
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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/index.html 6.37 KB
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Part 03-Module 01-Lesson 05_Support Vector Machines/06. Classification Error.html 6.38 KB
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Part 02-Module 03-Lesson 01_Model Selection/11. [Solution] Grid Search Lab.html 6.39 KB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question.html 6.39 KB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/01. Introduction.html 6.40 KB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review.html 6.40 KB
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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction.html 6.44 KB
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Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/img/submit-workspace.png 546.65 KB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.mp4 556.61 KB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.mp4 569.35 KB
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.mp4 573.82 KB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-3.png 575.91 KB
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.mp4 577.97 KB
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.mp4 595.52 KB
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Part 05-Module 01-Lesson 01_Neural Networks/img/and-to-or.png 606.14 KB
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Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/go.jpg 614.80 KB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-24-at-4.28.04-pm.png 622.69 KB
Part 02-Module 03-Lesson 01_Model Selection/img/models.png 627.96 KB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/actionvalue.png 628.42 KB
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Part 03-Module 01-Lesson 06_Ensemble Methods/img/screen-shot-2018-01-03-at-2.20.30-pm.png 647.38 KB
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.mp4 660.25 KB
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.mp4 692.80 KB
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.mp4 702.49 KB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4 708.93 KB
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Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-4.58.58-pm.png 716.00 KB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.mp4 732.40 KB
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.mp4 745.32 KB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/student-quiz.png 748.98 KB
Part 05-Module 01-Lesson 01_Neural Networks/img/student-quiz.png 748.98 KB
Part 01-Module 02-Lesson 01_Career Services Available to You/img/get-hired-with-the-udacity-career-portal.gif 756.73 KB
Part 04-Module 02-Lesson 01_Clustering/img/sebastian-katie-jay.png 779.77 KB
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.mp4 782.02 KB
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.mp4 803.69 KB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-terminal.gif 819.23 KB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.mp4 819.84 KB
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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/nature.png 893.03 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.mp4 927.05 KB
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Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.mp4 982.28 KB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/logistic-regression-quiz.png 984.45 KB
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.mp4 991.73 KB
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Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4 1001.40 KB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.mp4 1.01 MB
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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4 1.11 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.mp4 1.11 MB
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Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.mp4 1.13 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.13 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4 1.14 MB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.mp4 1.14 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.mp4 1.14 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.mp4 1.14 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.mp4 1.15 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4 1.15 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/convolutionalnetworksquiz.png 1.18 MB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.mp4 1.19 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/arch.png 1.20 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 09-Module 01-Lesson 02_LinkedIn Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 09-Module 02-Lesson 01_GitHub Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 10-Module 01-Lesson 05_Interview Practice/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.20 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.mp4 1.24 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw.mp4 1.24 MB
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Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.mp4 1.32 MB
Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.33 MB
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.mp4 1.34 MB
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Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.mp4 1.39 MB
Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4 1.41 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.mp4 1.45 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.mp4 1.46 MB
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.mp4 1.46 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.mp4 1.46 MB
Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.48 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.mp4 1.49 MB
Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.49 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/frozen-lake-6.jpg 1.50 MB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.mp4 1.50 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.mp4 1.52 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4 1.55 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.mp4 1.55 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.mp4 1.56 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/lesions.png 1.57 MB
Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.mp4 1.57 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4 1.57 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4 1.58 MB
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.mp4 1.58 MB
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.mp4 1.58 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.mp4 1.58 MB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4 1.59 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.mp4 1.60 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.mp4 1.60 MB
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.mp4 1.61 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4 1.62 MB
Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.62 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/skin-disease-classes.png 1.64 MB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.65 MB
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.mp4 1.67 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4 1.72 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.72 MB
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4 1.73 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.mp4 1.73 MB
Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.mp4 1.75 MB
Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4 1.75 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.mp4 1.76 MB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.mp4 1.80 MB
Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4 1.80 MB
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.mp4 1.81 MB
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.mp4 1.81 MB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.mp4 1.83 MB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 1.86 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.mp4 1.88 MB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 1.92 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.mp4 1.92 MB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 1.95 MB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 1.98 MB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4 1.99 MB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/run-main.gif 1.99 MB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.mp4 2.01 MB
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.mp4 2.05 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.mp4 2.05 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.mp4 2.06 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.mp4 2.07 MB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4 2.07 MB
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.08 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.mp4 2.14 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.mp4 2.14 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.mp4 2.14 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.mp4 2.15 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.mp4 2.15 MB
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.16 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.17 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4 2.19 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.mp4 2.20 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.mp4 2.22 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4 2.22 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.mp4 2.23 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4 2.23 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.mp4 2.24 MB
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.mp4 2.25 MB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.26 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.mp4 2.28 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.mp4 2.29 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.mp4 2.30 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4 2.30 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4 2.31 MB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.mp4 2.32 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.mp4 2.33 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.34 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.mp4 2.34 MB
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.mp4 2.35 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.mp4 2.38 MB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.mp4 2.38 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.mp4 2.40 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.42 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.mp4 2.43 MB
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.mp4 2.45 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.mp4 2.45 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.mp4 2.46 MB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.mp4 2.46 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.mp4 2.47 MB
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.mp4 2.48 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4 2.49 MB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.mp4 2.51 MB
Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.mp4 2.52 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.mp4 2.56 MB
Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.mp4 2.57 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4 2.58 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.mp4 2.58 MB
Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.59 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4 2.59 MB
Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.62 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.mp4 2.62 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4 2.65 MB
Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4 2.65 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4 2.68 MB
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.mp4 2.68 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.68 MB
Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.68 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.mp4 2.68 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.mp4 2.72 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.mp4 2.73 MB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-agent-monitor-main.gif 2.73 MB
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4 2.75 MB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.mp4 2.76 MB
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.mp4 2.77 MB
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.mp4 2.77 MB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.mp4 2.78 MB
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4 2.79 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.mp4 2.80 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.mp4 2.80 MB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.mp4 2.81 MB
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4 2.82 MB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.mp4 2.83 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 2.83 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.mp4 2.83 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4 2.83 MB
Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.mp4 2.84 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.mp4 2.85 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.mp4 2.85 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4 2.85 MB
Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.87 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.87 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 2.90 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 2.90 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.mp4 2.90 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4 2.91 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.mp4 2.96 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.mp4 2.96 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4 2.98 MB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.mp4 2.99 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.mp4 3.01 MB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.02 MB
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.03 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.mp4 3.07 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4 3.07 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.09 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.09 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4 3.09 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.mp4 3.09 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4 3.09 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.mp4 3.11 MB
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.mp4 3.15 MB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.mp4 3.15 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.mp4 3.16 MB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.mp4 3.17 MB
Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.20 MB
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.22 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.mp4 3.26 MB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.mp4 3.28 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.mp4 3.28 MB
Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.28 MB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.mp4 3.30 MB
Part 05-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.mp4 3.30 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.31 MB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4 3.34 MB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.mp4 3.35 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.mp4 3.35 MB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.mp4 3.36 MB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.mp4 3.37 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4 3.38 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4 3.42 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.mp4 3.44 MB
Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.mp4 3.44 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.mp4 3.45 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4 3.46 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4 3.46 MB
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.46 MB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.mp4 3.48 MB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.mp4 3.49 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.49 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.49 MB
Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.mp4 3.51 MB
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.54 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4 3.56 MB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.mp4 3.63 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4 3.63 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4 3.63 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.mp4 3.63 MB
Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.66 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.66 MB
Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.mp4 3.68 MB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.mp4 3.68 MB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.mp4 3.72 MB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.mp4 3.72 MB
Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.76 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.mp4 3.81 MB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 3.85 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4 3.85 MB
Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 3.85 MB
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.mp4 3.85 MB
Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.mp4 3.85 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.mp4 3.86 MB
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.mp4 3.87 MB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.mp4 3.87 MB
Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 3.90 MB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.mp4 3.91 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4 3.92 MB
Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.mp4 3.93 MB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.mp4 3.95 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 3.95 MB
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.mp4 3.98 MB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.mp4 3.99 MB
Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.mp4 3.99 MB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.01 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.mp4 4.05 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.mp4 4.09 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4 4.10 MB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4 4.11 MB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.mp4 4.13 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.mp4 4.14 MB
Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.14 MB
Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.16 MB
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.mp4 4.18 MB
Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.19 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.mp4 4.19 MB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.mp4 4.20 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4 4.22 MB
Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.22 MB
Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4 4.25 MB
Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.mp4 4.26 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.mp4 4.28 MB
Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.30 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4 4.34 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.mp4 4.38 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4 4.40 MB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.mp4 4.48 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.mp4 4.53 MB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4 4.57 MB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.59 MB
Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.mp4 4.61 MB
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.67 MB
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4 4.68 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.mp4 4.69 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.mp4 4.73 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4 4.73 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.mp4 4.74 MB
Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 4.80 MB
Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4 4.84 MB
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.mp4 4.86 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.mp4 4.87 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.mp4 4.93 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.mp4 4.95 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4 4.97 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.mp4 4.98 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.mp4 5.00 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4 5.02 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.04 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.04 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4 5.06 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.13 MB
Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.13 MB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.13 MB
Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.14 MB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.mp4 5.15 MB
Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.17 MB
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4 5.18 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4 5.20 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.mp4 5.28 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.33 MB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.35 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.37 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.mp4 5.39 MB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.41 MB
Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.41 MB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.41 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4 5.42 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.mp4 5.43 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.mp4 5.50 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.mp4 5.51 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 5.54 MB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.mp4 5.61 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4 5.61 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.mp4 5.63 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 5.69 MB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 5.75 MB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.mp4 5.75 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.mp4 5.78 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4 5.82 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4 5.85 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4 5.88 MB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.mp4 5.99 MB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.mp4 5.99 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.mp4 5.99 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4 6.00 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.mp4 6.00 MB
Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.01 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4 6.02 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4 6.04 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.mp4 6.05 MB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.09 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4 6.10 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4 6.13 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.mp4 6.14 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4 6.18 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.mp4 6.18 MB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.mp4 6.26 MB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.mp4 6.27 MB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.mp4 6.31 MB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.32 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.mp4 6.35 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.mp4 6.42 MB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.mp4 6.45 MB
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.mp4 6.48 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4 6.52 MB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.55 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.mp4 6.60 MB
Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.61 MB
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.mp4 6.63 MB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4 6.64 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.mp4 6.66 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.mp4 6.66 MB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4 6.68 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.mp4 6.74 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.mp4 6.79 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.mp4 6.79 MB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4 6.82 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.mp4 6.82 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4 6.84 MB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.mp4 6.85 MB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 6.92 MB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 6.92 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.mp4 6.92 MB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.mp4 6.97 MB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.mp4 6.99 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.mp4 6.99 MB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4 7.02 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.03 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.08 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.mp4 7.11 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.mp4 7.20 MB
Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.21 MB
Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.25 MB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.25 MB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.mp4 7.29 MB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.31 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4 7.36 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.mp4 7.36 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.mp4 7.37 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4 7.48 MB
Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.54 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg 7.54 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4 7.57 MB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.mp4 7.62 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4 7.63 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4 7.63 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4 7.64 MB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.mp4 7.71 MB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4 7.75 MB
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.mp4 7.77 MB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.mp4 7.86 MB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.mp4 7.86 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.mp4 7.88 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.mp4 7.94 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 7.96 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 7.98 MB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.00 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.04 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 8.05 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.09 MB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4 8.13 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4 8.14 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.mp4 8.17 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4 8.20 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.mp4 8.22 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4 8.24 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.mp4 8.33 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4 8.39 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4 8.39 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.mp4 8.42 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.mp4 8.45 MB
Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.mp4 8.46 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.mp4 8.48 MB
Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.49 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4 8.68 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 8.71 MB
Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 8.76 MB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.mp4 8.87 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.mp4 8.90 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.mp4 8.91 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 8.93 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4 9.08 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4 9.10 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.mp4 9.16 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4 9.17 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.20 MB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.20 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.20 MB
Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.23 MB
Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4 9.24 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.mp4 9.24 MB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.mp4 9.24 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4 9.26 MB
Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.33 MB
Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.mp4 9.44 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4 9.46 MB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4 9.47 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.49 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.49 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.49 MB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.mp4 9.59 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 9.69 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4 9.73 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.mp4 9.75 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.mp4 9.77 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4 9.80 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4 9.81 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 9.87 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.mp4 9.91 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4 9.96 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4 9.98 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4 10.01 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 10.07 MB
Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4 10.12 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4 10.22 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4 10.26 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.26 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4 10.30 MB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4 10.32 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4 10.38 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4 10.41 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4 10.45 MB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 10.53 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4 10.72 MB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.mp4 10.82 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4 11.03 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.25 MB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4 11.52 MB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.mp4 11.53 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.53 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.53 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.53 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4 11.70 MB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4 11.85 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4 11.97 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4 12.08 MB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4 12.12 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4 12.18 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4 12.18 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4 12.18 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4 12.24 MB
Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.34 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 12.46 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.mp4 12.48 MB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.mp4 12.50 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4 12.55 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 12.57 MB
Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 12.59 MB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4 12.62 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.64 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.64 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.64 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4 12.65 MB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 12.68 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.85 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.85 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.85 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 12.92 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 12.93 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 13.09 MB
Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.14 MB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.mp4 13.17 MB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.17 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.mp4 13.20 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.24 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.24 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.24 MB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4 13.26 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 13.32 MB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4 13.45 MB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.mp4 13.73 MB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.mp4 13.80 MB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4 14.02 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4 14.13 MB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4 14.28 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4 14.30 MB
Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 14.35 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4 14.38 MB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4 14.40 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4 14.59 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4 14.72 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4 14.75 MB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4 14.90 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.mp4 14.92 MB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.mp4 15.00 MB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4 15.20 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4 15.22 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 15.47 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4 15.65 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.mp4 16.13 MB
Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.mp4 16.40 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4 16.53 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4 16.53 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4 16.57 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4 16.64 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4 16.67 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.mp4 16.72 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4 16.90 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4 16.98 MB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4 17.04 MB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.mp4 17.13 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4 17.31 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4 17.37 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4 17.45 MB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.mp4 17.53 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 17.70 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.mp4 17.78 MB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.mp4 18.11 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 18.13 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 18.16 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4 18.22 MB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4 18.38 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4 18.41 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4 18.44 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 18.60 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 18.79 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4 18.85 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.mp4 19.74 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 19.81 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 19.91 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 19.97 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4 20.07 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4 20.08 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4 20.24 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 20.63 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4 20.67 MB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 20.72 MB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4 20.77 MB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4 20.88 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4 20.97 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4 21.03 MB
Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 21.06 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 21.37 MB
Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 21.68 MB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 21.79 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4 22.00 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4 22.01 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4 22.24 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4 22.51 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4 23.18 MB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 23.31 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4 23.41 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 24.68 MB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 25.04 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4 25.67 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4 26.30 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4 26.59 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 26.81 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4 27.57 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4 28.67 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4 30.11 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4 30.38 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4 31.04 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4 31.64 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4 31.66 MB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4 32.42 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4 32.51 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 32.54 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4 32.58 MB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4 32.69 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4 33.20 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4 33.39 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4 39.40 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4 40.68 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4 40.74 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4 43.55 MB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4 44.14 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4 48.38 MB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4 48.83 MB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4 49.74 MB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4 63.64 MB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.mp4 68.79 MB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.mp4 74.26 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4 104.62 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4 105.02 MB
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