[UdemyCourseDownloader] Machine Learning Engineer Nanodegree

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[UdemyCourseDownloader] Machine Learning Engineer Nanodegree

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文件列表

  • machine-learning-engineer-nanodegree.part1.rar 1.2 GB
  • machine-learning-engineer-nanodegree.part2.rar 1.2 GB
  • machine-learning-engineer-nanodegree.part3.rar 1.2 GB
  • machine-learning-engineer-nanodegree.part4.rar 1.2 GB
  • machine-learning-engineer-nanodegree.part5.rar 1.2 GB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4 110.1 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4 109.7 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.mp4 77.9 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.mp4 72.1 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4 66.7 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4 52.2 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4 51.2 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4 50.7 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4 46.3 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4 45.7 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 42.7 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4 42.7 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4 41.3 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4 35.0 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4 34.8 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4 34.3 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 34.2 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 34.1 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4 34.1 MB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4 34.0 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4 33.2 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 33.2 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4 32.6 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4 31.9 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4 31.6 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4 30.1 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4 28.9 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 28.1 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4 27.9 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 27.6 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4 26.9 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 26.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 25.9 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 24.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 24.4 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 24.3 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 23.6 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4 23.3 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4 23.1 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4 23.1 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 22.8 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 22.7 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 22.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 22.1 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4 22.0 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4 22.0 MB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4 21.9 MB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4 21.8 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 21.7 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4 21.7 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 21.6 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4 21.2 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4 21.1 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4 21.0 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 20.9 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 20.9 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 20.8 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 20.7 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4 19.8 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 19.7 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 19.5 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4 19.3 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4 19.3 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4 19.3 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4 19.1 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 19.0 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 19.0 MB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.mp4 19.0 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 18.6 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 18.6 MB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.mp4 18.4 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4 18.3 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4 18.2 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4 18.2 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.mp4 18.0 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4 17.9 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4 17.8 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4 17.7 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.mp4 17.5 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4 17.5 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4 17.5 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4 17.4 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4 17.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4 17.3 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.mp4 17.2 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.mp4 16.9 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4 16.4 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 16.2 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4 16.0 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4 15.9 MB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.mp4 15.7 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.mp4 15.6 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4 15.6 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4 15.5 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4 15.4 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4 15.3 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4 15.1 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4 15.1 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 15.1 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4 15.0 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4 15.0 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4 14.8 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4 14.7 MB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.mp4 14.5 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.mp4 14.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4 14.1 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 14.0 MB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4 13.9 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.mp4 13.8 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.8 MB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.mp4 13.8 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.8 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 13.7 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 13.6 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 13.6 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 13.3 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4 13.3 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4 13.2 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 13.2 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 13.2 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4 13.2 MB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.mp4 13.1 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.mp4 13.1 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 13.1 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.9 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4 12.8 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4 12.7 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4 12.7 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4 12.5 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4 12.4 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 12.3 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.mp4 12.1 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4 12.1 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.8 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4 11.6 MB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.mp4 11.3 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4 11.2 MB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 11.0 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4 11.0 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4 10.9 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4 10.9 MB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4 10.8 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4 10.8 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.8 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 10.8 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4 10.7 MB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4 10.6 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 10.6 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4 10.5 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4 10.5 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4 10.4 MB
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  • Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 10.4 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4 10.3 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4 10.3 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.mp4 10.2 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.mp4 10.2 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4 10.2 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 10.2 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.mp4 10.1 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
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  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4 9.9 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4 9.9 MB
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  • Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.7 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.6 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.6 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.6 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4 9.6 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 9.6 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4 9.5 MB
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  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 9.4 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.mp4 9.3 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.mp4 9.3 MB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.mp4 9.3 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 9.2 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 9.1 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4 9.1 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.9 MB
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  • Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.mp4 8.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.mp4 8.9 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.mp4 8.8 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 8.8 MB
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  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4 8.6 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.mp4 8.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4 8.6 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.mp4 8.6 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4 8.5 MB
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  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.5 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 8.4 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.4 MB
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  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 8.4 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 8.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.mp4 8.3 MB
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  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.mp4 8.2 MB
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  • Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4 8.1 MB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.mp4 8.1 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4 8.0 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4 8.0 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4 8.0 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.mp4 8.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4 7.9 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg 7.9 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 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 7.8 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.mp4 7.7 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.mp4 7.7 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4 7.7 MB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.7 MB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.mp4 7.6 MB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.6 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.6 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
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  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.mp4 7.5 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.4 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.4 MB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4 7.4 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 7.3 MB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.mp4 7.3 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.mp4 7.3 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.mp4 7.3 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 7.3 MB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 7.3 MB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.mp4 7.2 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4 7.2 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.mp4 7.2 MB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4 7.2 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.mp4 7.1 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.mp4 7.1 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.mp4 7.1 MB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4 7.0 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4 7.0 MB
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  • Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.9 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.mp4 6.9 MB
  • Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.9 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
  • Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.mp4 6.8 MB
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  • 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 6.7 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.mp4 6.7 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.6 MB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.mp4 6.6 MB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.mp4 6.6 MB
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  • Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.mp4 6.5 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4 6.5 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.mp4 6.4 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4 6.4 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4 6.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.4 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.mp4 6.3 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4 6.3 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4 6.3 MB
  • Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.3 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 6.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4 6.3 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.mp4 6.3 MB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.mp4 6.3 MB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.mp4 6.3 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4 6.2 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 6.1 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4 6.1 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.mp4 6.1 MB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.mp4 6.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.mp4 5.9 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4 5.9 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.mp4 5.9 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 5.8 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.mp4 5.8 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.mp4 5.8 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.mp4 5.7 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4 5.7 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.7 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.7 MB
  • Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.7 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.mp4 5.7 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.6 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.6 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.mp4 5.5 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4 5.5 MB
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4 5.4 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.4 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.mp4 5.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.4 MB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.4 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4 5.3 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4 5.3 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.mp4 5.2 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.mp4 5.2 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4 5.2 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.mp4 5.2 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.mp4 5.2 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.mp4 5.1 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.mp4 5.1 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4 5.1 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 5.0 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.mp4 5.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4 5.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.mp4 5.0 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.mp4 4.9 MB
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4 4.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.9 MB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.mp4 4.8 MB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.8 MB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4 4.8 MB
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  • 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 4.5 MB
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  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4 3.8 MB
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  • 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 3.6 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4 3.6 MB
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  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.mp4 3.5 MB
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  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.mp4 3.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4 3.5 MB
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  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.mp4 3.4 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.mp4 3.4 MB
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  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.mp4 3.3 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.mp4 3.3 MB
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  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.mp4 3.3 MB
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  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4 3.2 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.mp4 3.2 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4 3.2 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.2 MB
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  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4 3.2 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.mp4 3.2 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.2 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.2 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.mp4 3.2 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.mp4 3.1 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 3.1 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.mp4 3.1 MB
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  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4 3.1 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.mp4 3.0 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 3.0 MB
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  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
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  • Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4 3.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.mp4 3.0 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.mp4 3.0 MB
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  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4 3.0 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.mp4 3.0 MB
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  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4 3.0 MB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.mp4 2.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.mp4 2.9 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.mp4 2.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4 2.9 MB
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  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.mp4 2.9 MB
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.mp4 2.9 MB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.mp4 2.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4 2.9 MB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-agent-monitor-main.gif 2.9 MB
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  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.mp4 2.9 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.mp4 2.8 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
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  • Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4 2.8 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4 2.8 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.mp4 2.7 MB
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  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4 2.7 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.mp4 2.7 MB
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  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.mp4 2.6 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.mp4 2.6 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.mp4 2.6 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.mp4 2.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.mp4 2.5 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.5 MB
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  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.mp4 2.5 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.mp4 2.5 MB
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  • Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.5 MB
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  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.mp4 2.3 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.mp4 2.3 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4 2.3 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.3 MB
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  • Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt 2.9 kB
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  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.pt-BR.vtt 2.9 kB
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  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt 2.8 kB
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  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt 2.2 kB
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  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.en.vtt 2.1 kB
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  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.en.vtt 2.1 kB
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  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.pt-BR.vtt 2.0 kB
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  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.zh-CN.vtt 2.0 kB
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  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt 2.0 kB
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  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt 1.9 kB
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  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.zh-CN.vtt 1.8 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt 1.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.en.vtt 1.8 kB
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  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.zh-CN.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.pt-BR.vtt 1.8 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt 1.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.en.vtt 1.8 kB
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  • Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt 1.8 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.zh-CN.vtt 1.8 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • 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.en.vtt 1.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt 1.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
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  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt 1.8 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.ar.vtt 1.8 kB
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  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.zh-CN.vtt 1.8 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.en.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.pt-BR.vtt 1.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.pt-BR.vtt 1.8 kB
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  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt 1.8 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en.vtt 1.8 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.en.vtt 1.8 kB
  • Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.pt-BR.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.pt-BR.vtt 1.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
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  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt 1.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt 1.7 kB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.ar.vtt 1.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.pt-BR.vtt 1.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.en.vtt 1.7 kB
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  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.pt-BR.vtt 1.7 kB
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  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt 1.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt 1.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.pt-BR.vtt 1.7 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt 1.7 kB
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  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.pt-BR.vtt 1.7 kB
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  • 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.zh-CN.vtt 1.7 kB
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  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.en.vtt 1.7 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.en.vtt 1.7 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.en.vtt 1.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt 1.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.en.vtt 1.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.zh-CN.vtt 1.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.zh-CN.vtt 1.7 kB
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  • Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.zh-CN.vtt 1.7 kB
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  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.en.vtt 1.6 kB
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  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.pt-BR.vtt 1.6 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.en.vtt 1.6 kB
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  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en-US.vtt 1.6 kB
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  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en.vtt 1.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.pt-BR.vtt 1.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.es-MX.vtt 1.6 kB
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  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.pt-BR.vtt 1.5 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en-US.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.en.vtt 1.5 kB
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  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.pt-BR.vtt 1.5 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.pt-BR.vtt 1.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.pt-BR.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.pt-BR.vtt 1.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.zh-CN.vtt 1.5 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.es-MX.vtt 1.5 kB
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  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.zh-CN.vtt 1.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.pt-BR.vtt 1.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.en.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.pt-BR.vtt 1.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.pt-BR.vtt 1.5 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.zh-CN.vtt 1.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.en.vtt 1.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.en.vtt 1.5 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt 1.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt 1.5 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt 1.5 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.en.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en-US.vtt 1.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.pt-BR.vtt 1.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.en.vtt 1.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt 1.5 kB
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  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.zh-CN.vtt 1.5 kB
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  • 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.zh-CN.vtt 1.5 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt 1.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.zh-CN.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.en.vtt 1.5 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.zh-CN.vtt 1.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt 1.5 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.pt-BR.vtt 1.5 kB
  • 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 1.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.en.vtt 1.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.zh-CN.vtt 1.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.en.vtt 1.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt 1.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.zh-CN.vtt 1.4 kB
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  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.en.vtt 1.4 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.pt-BR.vtt 1.4 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.pt-BR.vtt 1.4 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.en.vtt 1.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt 1.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt 1.4 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.pt-BR.vtt 1.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.en.vtt 1.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.zh-CN.vtt 1.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.zh-CN.vtt 1.4 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt 1.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.en.vtt 1.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.pt-BR.vtt 1.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt 1.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt 1.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.en.vtt 1.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.en.vtt 1.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.zh-CN.vtt 1.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.pt-BR.vtt 1.4 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.pt-BR.vtt 1.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt 1.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.ar.vtt 1.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt 1.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.zh-CN.vtt 1.4 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt 1.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.pt-BR.vtt 1.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.en.vtt 1.4 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.zh-CN.vtt 1.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.en.vtt 1.4 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.en.vtt 1.4 kB
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.ar.vtt 1.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.zh-CN.vtt 1.4 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.ar.vtt 1.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.zh-CN.vtt 1.3 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.en.vtt 1.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en-US.vtt 1.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.ar.vtt 1.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en.vtt 1.3 kB
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  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt 1.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.zh-CN.vtt 1.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.pt-BR.vtt 1.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt 1.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.pt-BR.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.pt-BR.vtt 1.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.en.vtt 1.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.zh-CN.vtt 1.3 kB
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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.pt-BR.vtt 678 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 Bytes
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  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt 675 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt 672 Bytes
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  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.zh-CN.vtt 668 Bytes
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  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt 663 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt 663 Bytes
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  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt 657 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt.vtt 656 Bytes
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  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt 655 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.en.vtt 644 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt 643 Bytes
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt 643 Bytes
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  • 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 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt 633 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 Bytes
  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt 631 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.ar.vtt 624 Bytes
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  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt 618 Bytes
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt 615 Bytes
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  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.zh-CN.vtt 612 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 Bytes
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.en-US.vtt 608 Bytes
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.en.vtt 607 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt 607 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt 606 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt 601 Bytes
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  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.pt-BR.vtt 599 Bytes
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt 599 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.ar.vtt 597 Bytes
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  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt 595 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt 594 Bytes
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  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.pt-BR.vtt 592 Bytes
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.zh-CN.vtt 590 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt 590 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt 589 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt 588 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt 586 Bytes
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.zh-CN.vtt 584 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt 583 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.zh-CN.vtt 580 Bytes
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.en.vtt 579 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt 574 Bytes
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  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.ar.vtt 570 Bytes
  • Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.zh-CN.vtt 568 Bytes
  • 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 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.ar.vtt 561 Bytes
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.zh-CN.vtt 560 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.ar.vtt 559 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt 558 Bytes
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt 557 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt 555 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 Bytes
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.pt-BR.vtt 549 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt 543 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 Bytes
  • Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.en.vtt 540 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt 540 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt 540 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt 538 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 Bytes
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.zh-CN.vtt 535 Bytes
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt 533 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt 530 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt 526 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt 524 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt 521 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.pt-BR.vtt 518 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt 517 Bytes
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.en.vtt 514 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt 514 Bytes
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.pt-BR.vtt 512 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.ar.vtt 512 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.ar.vtt 510 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt 510 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt 508 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.zh-CN.vtt 507 Bytes
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.pt-BR.vtt 507 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.ar.vtt 505 Bytes
  • 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 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.en.vtt 501 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.zh-CN.vtt 499 Bytes
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.zh-CN.vtt 498 Bytes
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  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.ar.vtt 490 Bytes
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  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.en.vtt 489 Bytes
  • Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.zh-CN.vtt 488 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt 488 Bytes
  • 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 Bytes
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.zh-CN.vtt 485 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.en.vtt 483 Bytes
  • 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 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.ar.vtt 479 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt 477 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.en.vtt 476 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt 475 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.pt-BR.vtt 474 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.en.vtt 473 Bytes
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  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.en.vtt 472 Bytes
  • 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 Bytes
  • 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 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt 467 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt 466 Bytes
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  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.en.vtt 458 Bytes
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.en.vtt 457 Bytes
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  • 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 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt 454 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.pt-BR.vtt 454 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt 451 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.ar.vtt 444 Bytes
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.zh-CN.vtt 440 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt 439 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 Bytes
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt 437 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt 432 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.pt-BR.vtt 426 Bytes
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  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.pt-BR.vtt 421 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
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  • 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 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.en.vtt 419 Bytes
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  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.en.vtt 418 Bytes
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  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.zh-CN.vtt 408 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.en.vtt 406 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.pt-BR.vtt 402 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.en.vtt 399 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.zh-CN.vtt 396 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.en.vtt 395 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.ar.vtt 393 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt 385 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt 385 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt 370 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt 369 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.zh-CN.vtt 369 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.en.vtt 368 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.pt-BR.vtt 362 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt 361 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.zh-CN.vtt 361 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.ar.vtt 360 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.ar.vtt 359 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.ar.vtt 357 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt 355 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt 355 Bytes
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.zh-CN.vtt 342 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.zh-CN.vtt 335 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt 332 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.pt-BR.vtt 331 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt 326 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.pt-BR.vtt 326 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.en.vtt 325 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.en.vtt 325 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt 324 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.en.vtt 320 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.zh-CN.vtt 316 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt 315 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.en.vtt 312 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt 309 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt 306 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.ar.vtt 306 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.zh-CN.vtt 305 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en.vtt 303 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.pt-BR.vtt 302 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.ar.vtt 301 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt 301 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.zh-CN.vtt 299 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.en.vtt 298 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.en.vtt 292 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.pt-BR.vtt 292 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.ar.vtt 284 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.ar.vtt 282 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt 277 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.zh-CN.vtt 277 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.en.vtt 273 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.pt-BR.vtt 271 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.ar.vtt 258 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.zh-CN.vtt 245 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.zh-CN.vtt 243 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.pt-BR.vtt 233 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.zh-CN.vtt 232 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.pt-BR.vtt 230 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.en.vtt 229 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.ar.vtt 226 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.pt-BR.vtt 226 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.zh-CN.vtt 222 Bytes
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  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.en.vtt 208 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.en.vtt 207 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt 206 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.en.vtt 205 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt 204 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.ar.vtt 204 Bytes
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.ar.vtt 203 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.pt-BR.vtt 186 Bytes
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  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.ar.vtt 171 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.pt-BR.vtt 171 Bytes
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  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.zh-CN.vtt 167 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.zh-CN.vtt 166 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.zh-CN.vtt 166 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.zh-CN.vtt 165 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.pt-BR.vtt 164 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.en.vtt 164 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.pt-BR.vtt 143 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.en.vtt 141 Bytes
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.pt-BR.vtt 141 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.en.vtt 140 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.ar.vtt 140 Bytes
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.en.vtt 139 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.en.vtt 138 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.zh-CN.vtt 125 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.zh-CN.vtt 125 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.pt-BR.vtt 124 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.ar.vtt 122 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt 118 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt 113 Bytes
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  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt 108 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt 107 Bytes
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  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt 104 Bytes

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