Udacity - Machine Learning Engineer Nanodegree nd009t v1.0.0
File List
- Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4 105.0 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4 104.6 MB
- Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.mp4 74.3 MB
- Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.mp4 68.8 MB
- Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4 63.6 MB
- Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4 49.7 MB
- Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4 48.8 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4 48.4 MB
- Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4 44.1 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4 43.5 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4 40.7 MB
- Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4 40.7 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4 39.4 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4 33.4 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4 33.2 MB
- Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4 32.7 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4 32.6 MB
- Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 32.5 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4 32.5 MB
- Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4 32.4 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4 31.7 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4 31.6 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4 31.0 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4 30.4 MB
- Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4 30.1 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4 28.7 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4 27.6 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 26.8 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4 26.6 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4 26.3 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4 25.7 MB
- Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 25.0 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 24.7 MB
- Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4 23.4 MB
- Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 23.3 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4 23.2 MB
- Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4 22.5 MB
- Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4 22.2 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4 22.0 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4 22.0 MB
- Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 21.8 MB
- Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 21.7 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 21.4 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 21.1 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4 21.0 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4 21.0 MB
- Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4 20.9 MB
- Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4 20.8 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 20.7 MB
- Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4 20.7 MB
- Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 20.6 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4 20.2 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4 20.1 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4 20.1 MB
- Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 20.0 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 19.9 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 19.8 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 19.7 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4 18.9 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 18.8 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 18.6 MB
- Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4 18.4 MB
- Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4 18.4 MB
- Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4 18.4 MB
- Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4 18.2 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 18.2 MB
- Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 18.1 MB
- Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.mp4 18.1 MB
- Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.mp4 17.8 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 17.7 MB
- Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.mp4 17.5 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4 17.4 MB
- Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4 17.4 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4 17.3 MB
- Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.mp4 17.1 MB
- Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4 17.0 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4 17.0 MB
- Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4 16.9 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.mp4 16.7 MB
- Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4 16.7 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4 16.6 MB
- Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4 16.6 MB
- Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4 16.5 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4 16.5 MB
- Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.mp4 16.4 MB
- Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.mp4 16.1 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4 15.6 MB
- Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 15.5 MB
- Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4 15.2 MB
- Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4 15.2 MB
- Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.mp4 15.0 MB
- Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.mp4 14.9 MB
- Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4 14.9 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4 14.7 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4 14.7 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4 14.6 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4 14.4 MB
- Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4 14.4 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 14.4 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4 14.3 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4 14.3 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4 14.1 MB
- Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4 14.0 MB
- Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.mp4 13.8 MB
- Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.mp4 13.7 MB
- Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4 13.4 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 13.3 MB
- Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4 13.3 MB
- Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.2 MB
- Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.2 MB
- Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.2 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.mp4 13.2 MB
- Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.2 MB
- Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.mp4 13.2 MB
- Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.1 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 13.1 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 12.9 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 12.9 MB
- Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.9 MB
- Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.9 MB
- Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4 12.9 MB
- Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 12.7 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4 12.7 MB
- Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.6 MB
- Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.6 MB
- Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 12.6 MB
- Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4 12.6 MB
- Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 12.6 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 12.6 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4 12.5 MB
- Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.mp4 12.5 MB
- Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.mp4 12.5 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 12.5 MB
- Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.3 MB
- Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4 12.2 MB
- Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4 12.2 MB
- Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4 12.2 MB
- Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4 12.2 MB
- Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4 12.1 MB
- Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4 12.1 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4 12.0 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4 11.9 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4 11.7 MB
- Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.5 MB
- Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.5 MB
- Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 11.5 MB
- Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.mp4 11.5 MB
- Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4 11.5 MB
- Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.3 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4 11.0 MB
- Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.mp4 10.8 MB
- Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4 10.7 MB
- Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 10.5 MB
- Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4 10.4 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4 10.4 MB
- Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4 10.4 MB
- Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4 10.3 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4 10.3 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.3 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 10.3 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4 10.2 MB
- Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4 10.1 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 10.1 MB
- Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4 10.0 MB
- Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4 10.0 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4 10.0 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.mp4 9.9 MB
- Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 9.9 MB
- Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4 9.8 MB
- Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4 9.8 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.mp4 9.8 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.mp4 9.7 MB
- Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4 9.7 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 9.7 MB
- Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.mp4 9.6 MB
- Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.5 MB
- Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.5 MB
- Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.5 MB
- Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4 9.5 MB
- Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4 9.5 MB
- Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.mp4 9.4 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.3 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4 9.3 MB
- Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.mp4 9.2 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.mp4 9.2 MB
- Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4 9.2 MB
- Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.2 MB
- Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.2 MB
- Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.2 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.2 MB
- Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4 9.2 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 9.2 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4 9.1 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4 9.1 MB
- Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 8.9 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.mp4 8.9 MB
- Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.mp4 8.9 MB
- Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.mp4 8.9 MB
- Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 8.8 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 8.7 MB
- Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4 8.7 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.5 MB
- Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.mp4 8.5 MB
- Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.mp4 8.5 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.mp4 8.5 MB
- Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.mp4 8.4 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 8.4 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4 8.4 MB
- Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.mp4 8.3 MB
- Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4 8.2 MB
- Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.mp4 8.2 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4 8.2 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.mp4 8.2 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4 8.1 MB
- Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4 8.1 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.1 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 8.0 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.0 MB
- Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.0 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 8.0 MB
- Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 8.0 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.mp4 7.9 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.mp4 7.9 MB
- Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.mp4 7.9 MB
- Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.mp4 7.9 MB
- Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.mp4 7.8 MB
- Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4 7.8 MB
- Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.mp4 7.7 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4 7.6 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4 7.6 MB
- Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4 7.6 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.mp4 7.6 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4 7.6 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg 7.5 MB
- Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.5 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 7.5 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.mp4 7.4 MB
- Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.mp4 7.4 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4 7.4 MB
- Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.3 MB
- Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.mp4 7.3 MB
- Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.2 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.2 MB
- Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.2 MB
- Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.mp4 7.2 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.mp4 7.1 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.1 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.0 MB
- Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4 7.0 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 7.0 MB
- Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.mp4 7.0 MB
- Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.mp4 7.0 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.mp4 6.9 MB
- Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 6.9 MB
- Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 6.9 MB
- Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.mp4 6.8 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4 6.8 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.mp4 6.8 MB
- Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4 6.8 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.mp4 6.8 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.mp4 6.8 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.mp4 6.7 MB
- Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4 6.7 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.mp4 6.7 MB
- Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.mp4 6.7 MB
- Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4 6.6 MB
- Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.mp4 6.6 MB
- Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.6 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.mp4 6.6 MB
- Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.6 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4 6.5 MB
- Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.mp4 6.5 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.mp4 6.5 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.mp4 6.4 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.mp4 6.4 MB
- Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.3 MB
- Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.mp4 6.3 MB
- Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.mp4 6.3 MB
- Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.mp4 6.3 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.mp4 6.2 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4 6.2 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.mp4 6.1 MB
- Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4 6.1 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4 6.1 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.1 MB
- Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.mp4 6.0 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4 6.0 MB
- Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4 6.0 MB
- Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.0 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 6.0 MB
- Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4 6.0 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.mp4 6.0 MB
- Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.mp4 6.0 MB
- Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.mp4 6.0 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4 5.9 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4 5.9 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4 5.8 MB
- Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.mp4 5.8 MB
- Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.mp4 5.7 MB
- Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 5.7 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 5.7 MB
- Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.mp4 5.6 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4 5.6 MB
- Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.mp4 5.6 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 5.5 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.mp4 5.5 MB
- Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.mp4 5.5 MB
- Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.mp4 5.4 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4 5.4 MB
- Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.4 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.4 MB
- Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.4 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.mp4 5.4 MB
- Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.4 MB
- Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.3 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.3 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.mp4 5.3 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4 5.2 MB
- Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4 5.2 MB
- Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.2 MB
- Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.mp4 5.2 MB
- Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.1 MB
- Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.1 MB
- Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.1 MB
- Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.1 MB
- Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4 5.1 MB
- Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.0 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.0 MB
- Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4 5.0 MB
- Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.mp4 5.0 MB
- Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.mp4 5.0 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4 5.0 MB
- Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.mp4 4.9 MB
- Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.mp4 4.9 MB
- Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.mp4 4.9 MB
- Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.mp4 4.9 MB
- Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4 4.8 MB
- Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 4.8 MB
- Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.mp4 4.7 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4 4.7 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.mp4 4.7 MB
- Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.mp4 4.7 MB
- Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4 4.7 MB
- Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.7 MB
- Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.mp4 4.6 MB
- Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.6 MB
- Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4 4.6 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.mp4 4.5 MB
- Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.mp4 4.5 MB
- Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4 4.4 MB
- Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.mp4 4.4 MB
- Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4 4.3 MB
- Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.3 MB
- Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.mp4 4.3 MB
- Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.mp4 4.3 MB
- Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4 4.3 MB
- Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.2 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4 4.2 MB
- Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.mp4 4.2 MB
- Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.mp4 4.2 MB
- Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.2 MB
- Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.mp4 4.2 MB
- Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.2 MB
- Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.1 MB
- Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.mp4 4.1 MB
- Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.mp4 4.1 MB
- Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4 4.1 MB
- Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4 4.1 MB
- Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.mp4 4.1 MB
- Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.mp4 4.0 MB
- Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.0 MB
- Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.mp4 4.0 MB
- Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.mp4 4.0 MB
- Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.mp4 4.0 MB
- Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.0 MB
- Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.mp4 4.0 MB
- Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.mp4 3.9 MB
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- Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.4 MB
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- Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4 1.6 MB
- Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.6 MB
- Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.mp4 1.6 MB
- Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.mp4 1.6 MB
- Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.mp4 1.6 MB
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- Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.mp4 1.6 MB
- Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.mp4 1.6 MB
- Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.mp4 1.6 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4 1.6 MB
- Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4 1.6 MB
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