[FCO] D.S.ND v1.0.0
File List
- Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4 40.7 MB
- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/02. What Do Data Scientists at AirBnB Do-q7sw9vc5o1U.mp4 40.2 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/24. Data Engineering-z6r2e_V0Td0.mp4 35.3 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Py Part 2 V1-u50_ZyKqt8g.mp4 34.6 MB
- Part 04-Module 01-Lesson 03_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 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.mp4 32.5 MB
- Part 04-Module 01-Lesson 03_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 03-Module 01-Lesson 05_Deep Learning with PyTorch/05. Py Part 3 V2-u8hDj5aJK6I.mp4 28.4 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/09. L1 06 How To Succeed REPLACEMENT-JRnZOZR97QQ.mp4 27.1 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/07. L1 06 How To Succeed REPLACEMENT-JRnZOZR97QQ.mp4 27.1 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/07. Py Part 5 V2-coBbbrGZXI0.mp4 27.1 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 26.8 MB
- Part 01-Module 03-Lesson 01_What Is Ahead/03. Rachel from Kaggle-uVsYYzxbyIg.mp4 26.4 MB
- Part 15-Module 01-Lesson 06_Web Development/14. Bootstrap Library-KsrqjguHWUI.mp4 26.4 MB
- Part 04-Module 01-Lesson 03_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 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/03. Dan Frank Interview-Me-KRvZW1QQ.mp4 26.1 MB
- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/04. DSND T2 Intro Dan Frank V4-rTCPmVQDsEw.mp4 25.8 MB
- Part 05-Module 01-Lesson 01_Congratulations!/04. Arvato Final Project-qBR6A0IQXEE.mp4 25.4 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Arvato Final Project-qBR6A0IQXEE.mp4 25.4 MB
- Part 05-Module 01-Lesson 01_Congratulations!/04. Introduction to Blogging for Data Science-WrvGpRN5XQI.mp4 25.2 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Introduction to Blogging for Data Science-WrvGpRN5XQI.mp4 25.2 MB
- Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/01. Blogging for Data Science-WrvGpRN5XQI.mp4 25.2 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.mp4 25.0 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/10. Py Part 8 V1-3eqn5sgCOsY.mp4 24.9 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/13. How to Break Into the Field Solution-Db_2Lmwo4EY.mp4 24.5 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.mp4 23.7 MB
- Part 04-Module 01-Lesson 02_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 01-Lesson 03_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 12-Module 01-Lesson 16_Logistic Regression/29. Model Diagnostics In Python-1Z4eorbfOOc.mp4 22.8 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4 22.5 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.mp4 22.1 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.mp4 22.0 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/40. Categorical Variables-p3gDUkBD9uM.mp4 21.8 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/05. Interview with Art - Part 1-ClLYamtaO-Q.mp4 21.8 MB
- Part 02-Module 01-Lesson 04_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 21.7 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.mp4 21.6 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 21.1 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.mp4 21.0 MB
- Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/09. Linear Transformations 1-99jYIxBRDww.mp4 20.8 MB
- Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.mp4 20.6 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/33. Data Engineering Importance-VO-OrJ0JqxM.mp4 20.6 MB
- Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.mp4 20.5 MB
- Part 04-Module 01-Lesson 04_PCA/12. 11 PCA 1 Solution V1-u0rJRmubQ44.mp4 20.2 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 03362 V1-MwRSg5RASoc.mp4 20.2 MB
- Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/11. Linear Transformations 3-g_yTyRwMzXU.mp4 20.1 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 20.0 MB
- Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.mp4 19.9 MB
- Part 04-Module 01-Lesson 03_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 16-Module 01-Lesson 03_NLP Pipelines/04. Cleaning-qawXp9DPV6I.mp4 19.6 MB
- Part 15-Module 01-Lesson 06_Web Development/30. Deployment-YPfNzpnm_Rk.mp4 19.4 MB
- Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.mp4 18.9 MB
- Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/04. Experimental Design Insights With Richard Sharp-XDBw2nfOrsU.mp4 18.9 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 1051320 V1-_4N6h82szWo.mp4 18.9 MB
- Part 01-Module 03-Lesson 01_What Is Ahead/02. Adam from IBM-NjjtY5UHyac.mp4 18.9 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 18.8 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 18.6 MB
- Part 06-Module 01-Lesson 03_Control Flow/10. For Loops-UtX0PXSUCdY.mp4 18.4 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/05. How Does MLR Work-bvM6eUYyurA.mp4 18.4 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/35. Imputation Methods-OwEWSBitF-Q.mp4 18.4 MB
- Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.mp4 18.3 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 18.1 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.mp4 18.0 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 11123244 V1-QlILlYuWF9U.mp4 17.9 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.mp4 17.8 MB
- Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/08. Advanced API Code Walk-through-AkqO534YooE.mp4 17.7 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/32. Removing Data Part II-lPl6-Z098Rs.mp4 17.6 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/17. Multicollinearity VIFs-wbtrXMusDe8.mp4 17.5 MB
- Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.mp4 17.4 MB
- Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.mp4 17.4 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/21. Predicting Salary-HTp4LA1MJh8.mp4 17.3 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/02. Meet The Instructors-XAU2Nf51vfU.mp4 17.3 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/22. Interactions Higher Order Terms-gMHwogzqPOk.mp4 17.3 MB
- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Subquery Solution Video-Y6S3S0LsMrw.mp4 17.3 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.mp4 17.3 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/13. Strings-ySZDrs-nNqg.mp4 17.3 MB
- Part 01-Module 03-Lesson 01_What Is Ahead/04. What'S Ahead Figure 8 Fix-SE4TQnOwmBI.mp4 17.1 MB
- Part 15-Module 01-Lesson 06_Web Development/27. 40 Screencast Flask Pandas Plotly Part4 V2-4IF2G9Fehb4.mp4 17.1 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 01725 V1-UFmfDAiaOmw.mp4 17.0 MB
- Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.mp4 17.0 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.mp4 16.9 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.mp4 16.8 MB
- Part 15-Module 01-Lesson 01_Introduction to Software Engineering/04. L1 04 Meet Juno V1 V2-c4r2nGMogfM.mp4 16.6 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.mp4 16.4 MB
- Part 04-Module 01-Lesson 06_Project Identify Customer Segments/01. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.mp4 16.4 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/07. A Look at the Data-vPHVUYvCNGE.mp4 16.4 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/27. 20 Putting Code On PyPi V1-4uosDOKn5LI.mp4 16.3 MB
- Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks/01. Starbucks Lab-QPKRboscAf4.mp4 16.2 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/08. Dummy Variables--QTgDd-fZuA.mp4 16.2 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/43. Putting It All Together-3SX4dMZPNEI.mp4 16.0 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.mp4 15.9 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/08. Py Part 6 V1-HiTih59dCWQ.mp4 15.9 MB
- Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.mp4 15.9 MB
- Part 15-Module 01-Lesson 06_Web Development/10. CSS-s_sdzHR9cs0.mp4 15.9 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.mp4 15.8 MB
- Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.mp4 15.7 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/17. Job Satisfaction-OjCNMhWlYh8.mp4 15.5 MB
- Part 06-Module 01-Lesson 04_Functions/02. Defining Functions-IP_tJYhynbc.mp4 15.5 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 15.5 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/08. Números inteiros e floats-MiJ1vfWp-Ts.mp4 15.4 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.mp4 15.3 MB
- Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.mp4 15.1 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.mp4 15.0 MB
- Part 01-Module 03-Lesson 01_What Is Ahead/01. What Do Data Scientists Do-sN2DbIJUZmw.mp4 15.0 MB
- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/01. What Do Data Scientists Do-sN2DbIJUZmw.mp4 15.0 MB
- Part 15-Module 01-Lesson 06_Web Development/16. 18 Screencast Plotly V2-QsmOW1jNeio.mp4 14.8 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/09. PyTorch - Part 7-hFu7GTfRWks.mp4 14.6 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.mp4 14.6 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.mp4 14.4 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.mp4 14.4 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 14.4 MB
- Part 06-Module 01-Lesson 06_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.mp4 14.2 MB
- Part 11-Module 01-Lesson 03_Linear Combination/02. Linear Combinations 2-RsKJNDTb8nw.mp4 14.2 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/03. Part 1 V2-n4mbZYIfKb4.mp4 13.8 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/02. Multiple Linear Regression-rvYZp99nj6c.mp4 13.8 MB
- Part 05-Module 01-Lesson 01_Congratulations!/04. Figure 8 Project-QbLVh5GTuJQ.mp4 13.6 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Figure 8 Project-QbLVh5GTuJQ.mp4 13.6 MB
- Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/01. Figure 8 Project-QbLVh5GTuJQ.mp4 13.6 MB
- Part 05-Module 01-Lesson 01_Congratulations!/04. Build A Recommendation Engine IBM-A0rVwTbntf4.mp4 13.5 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Build A Recommendation Engine IBM-A0rVwTbntf4.mp4 13.5 MB
- Part 17-Module 04-Lesson 01_Recommendation Engines/01. IBM Project Overview-XP_f64c07Gc.mp4 13.5 MB
- Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/10. Linear Transformations 2-imtEd8M6__s.mp4 13.5 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/11. Dummy Variable Interpretation-TxP_TD0kbOo.mp4 13.4 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 06 Lists Methods V1-tz2Ja1Eaeqo.mp4 13.3 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.mp4 13.3 MB
- Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.mp4 13.3 MB
- Part 06-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.mp4 13.2 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.2 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/37. Imputing Values-nTM4HiDneeE.mp4 13.2 MB
- Part 02-Module 01-Lesson 04_Decision Trees/15. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.1 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 11442204 V1-8kdRNQnqSGA.mp4 13.1 MB
- Part 05-Module 01-Lesson 01_Congratulations!/03. Next Steps-kXMCKZ4HqsM.mp4 13.0 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 12.9 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.mp4 12.9 MB
- Part 04-Module 01-Lesson 04_PCA/10. 09 PCA V1-0RLDZWeq5JE.mp4 12.8 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 18003113 V1-2M-WX2X2ts4.mp4 12.8 MB
- Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.mp4 12.8 MB
- Part 16-Module 01-Lesson 01_Introduction to Data Engineering/03. Figure 8 Project V2-adtlHL42AuQ.mp4 12.6 MB
- Part 02-Module 01-Lesson 04_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 12.6 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 33514421 V1-TCaeEdrbYRc.mp4 12.6 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 12.6 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.mp4 12.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.mp4 12.5 MB
- Part 02-Module 01-Lesson 04_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.3 MB
- Part 10-Module 01-Lesson 03_Review a Repo's History/01. A Repository's History - Intro-UBmg3syQS0E.mp4 12.3 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/23. What Happened-gLn6_Z3nwcc.mp4 12.1 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/24. What If Our Sample Is Large-WoTCeSTL1eM.mp4 12.0 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.mp4 11.9 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.mp4 11.8 MB
- Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4 11.7 MB
- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.mp4 11.6 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.mp4 11.5 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/08. Types Of Errors - Part II-mbdSQ5CjdFs.mp4 11.5 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.mp4 11.4 MB
- Part 06-Module 01-Lesson 05_Scripting/20. Importing Files-qjeSn6zZbR0.mp4 11.4 MB
- Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.mp4 11.3 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/18. Multicollinearity VIFs-uiF3UcDWwPI.mp4 11.3 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.mp4 11.3 MB
- Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.mp4 11.3 MB
- Part 03-Module 01-Lesson 02_Implementing Gradient Descent/03. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.3 MB
- Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.mp4 11.2 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.mp4 11.2 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.mp4 11.1 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.mp4 11.0 MB
- Part 15-Module 01-Lesson 01_Introduction to Software Engineering/01. Introduction To Software Engineering-7kphieW4yl4.mp4 11.0 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.mp4 11.0 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.mp4 10.9 MB
- Part 06-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.mp4 10.9 MB
- Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.mp4 10.8 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Common Types of Hypothesis Tests-8hv8KnvQ6JY.mp4 10.8 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.mp4 10.8 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/01. Shell Intro--EtN5oD8MM0.mp4 10.8 MB
- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/09. SQL Subquery Video-10pmKmTI_CA.mp4 10.7 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.mp4 10.7 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.mp4 10.7 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.mp4 10.7 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/14. 14 Funk SVD-H8gdwXy_npI.mp4 10.7 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 4321430 V1-zVGhBQNgbc4.mp4 10.6 MB
- Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.mp4 10.5 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/24. Recommendations 2 25 V1-zgz5WYlI5fE.mp4 10.5 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.mp4 10.4 MB
- Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.mp4 10.3 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 14502145 V1-cvQngTUOWbM.mp4 10.3 MB
- Part 15-Module 01-Lesson 06_Web Development/05. 6 Screencast HTML Code V2-G7fBus1JSc0.mp4 10.3 MB
- Part 04-Module 01-Lesson 03_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 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.mp4 10.2 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/14. Bootcamps-l2tYmee3kxo.mp4 10.2 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Kaggle Project Final For Classroom-Ssttix340C8.mp4 10.2 MB
- Part 02-Module 01-Lesson 10_Finding Donors Project/06. Kaggle Project Final For Classroom-Ssttix340C8.mp4 10.2 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.mp4 10.1 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/11. Meet the Careers Team-cuKecPpZ7PM.mp4 10.1 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/08. Meet the Careers Team-cuKecPpZ7PM.mp4 10.1 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.mp4 10.1 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.mp4 10.1 MB
- Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.mp4 10.0 MB
- Part 04-Module 01-Lesson 05_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 9.9 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.mp4 9.9 MB
- Part 04-Module 01-Lesson 01_Clustering/11. 12 KMeans In Scikit Learn Solution V1-IIVsWFq2DXk.mp4 9.8 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/02. L2 02 Clean Mod Code Vid 1 V1 V2-RjHV8kRpVbA.mp4 9.8 MB
- Part 06-Module 01-Lesson 06_NumPy/08. NumPy 4 V1-jeU7lLgyMms.mp4 9.8 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/09. SVD-t2XTuHq6-xc.mp4 9.8 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/10. Data Ink Ratio-gW2FapuYV4A.mp4 9.8 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/02. Fitting Logistic Regression-Dg0rBDQnIYg.mp4 9.7 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/04. Fitting Logistic Regression In Python-baQf-XiZQQ4.mp4 9.7 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 9.7 MB
- Part 15-Module 01-Lesson 06_Web Development/12. 14 Screencast JavaScript V2-vgXUKgsT_48.mp4 9.7 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.mp4 9.7 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/26. Scikitlearn Source Code-4_qkqMsbthg.mp4 9.6 MB
- Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.mp4 9.6 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.mp4 9.6 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.mp4 9.4 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.3 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/05. Using Workspaces-45N9NK6kQ0Y.mp4 9.3 MB
- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.mp4 9.3 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4 9.3 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.mp4 9.3 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/03. World Bank Datasets-lNPzOLzZVbw.mp4 9.3 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.mp4 9.2 MB
- Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.mp4 9.2 MB
- Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.mp4 9.2 MB
- Part 02-Module 01-Lesson 09_Training and Tuning/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.2 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 10131720 V1-DWHYK0XSI70.mp4 9.2 MB
- Part 04-Module 01-Lesson 05_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.2 MB
- Part 02-Module 01-Lesson 07_Ensemble Methods/03. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.2 MB
- Part 04-Module 01-Lesson 04_PCA/15. 14 Interpretation Solution V1-wU2duZa0ds0.mp4 9.2 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.mp4 9.2 MB
- Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.mp4 9.1 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/01. Welcome To DSND T2 V1 1 V1-ebJZrc2y85Q.mp4 9.1 MB
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- Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.mp4 9.0 MB
- Part 04-Module 01-Lesson 01_Clustering/09. 10 KMeans In Scikit Learn V1-jkEgQLOcCGo.mp4 9.0 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 36044330 V1-b5gFe8Ij-g0.mp4 9.0 MB
- Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 8.9 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.mp4 8.9 MB
- Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 01 Welcome-lbR82UD5F0c.mp4 8.9 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 0950 V1-yrNZ0sQwNcs.mp4 8.9 MB
- Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.mp4 8.9 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 012725 V1-Y1dN-mB39rM.mp4 8.8 MB
- Part 02-Module 01-Lesson 02_Linear Regression/26. Regularization-PyFNIcsNma0.mp4 8.8 MB
- Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/01. C4 Intro-gXlqR86h0yI.mp4 8.7 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 20332637 V1-UnDocJ9VUec.mp4 8.7 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 10491855 V2-pjoxB00grHw.mp4 8.6 MB
- Part 11-Module 01-Lesson 01_Introduction/03. Essence Of Linear Algebra Intro -EHcxDZpeGFg.mp4 8.6 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/20. Connecting Errors and P-Values-hFNjd5l9CLs.mp4 8.6 MB
- Part 06-Module 01-Lesson 06_NumPy/07. NumPy 3 V1-Rt4aydeo9F8.mp4 8.5 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/11. How To Break Into The Field-0-Y39LZ80VE.mp4 8.5 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.5 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/14. Using Color-6bAedqD3ilw.mp4 8.5 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 4381128 V1-B6bELCg6gMs.mp4 8.5 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/33. Imputing Missing Values-CEWIPjz_gCE.mp4 8.4 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/09. L2 06 Efficient Code V1 V2-LbtxY7xetBw.mp4 8.4 MB
- Part 04-Module 01-Lesson 03_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 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4 8.4 MB
- Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.mp4 8.4 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 4422330 V1-DJfwhP_vvh4.mp4 8.4 MB
- Part 16-Module 01-Lesson 01_Introduction to Data Engineering/01. 01 Welcome V1 V2-Ykd7CN5dDx0.mp4 8.4 MB
- Part 10-Module 01-Lesson 07_Working With Remotes/01. Intro-SBUOhyXcR1Q.mp4 8.3 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.mp4 8.3 MB
- Part 11-Module 01-Lesson 03_Linear Combination/01. Linear Combinations 1-fmal7UE7dEE.mp4 8.3 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/04. Object Oriented Programming Syntax-Y8ZVw1LHI8E.mp4 8.3 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.mp4 8.3 MB
- Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/04. Exploratory vs. Explanatory Analysis-wvgBSMks4p8.mp4 8.2 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.mp4 8.2 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/04. 01 Writing Clean Code V1-wNaiahWCwkQ.mp4 8.2 MB
- Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Action-cL6ehKtJLUM.mp4 8.2 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 10491855 V1-BafXxtTuZgQ.mp4 8.1 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/10. 03 Optimizing Common Books V1-WF9n_19V08g.mp4 8.1 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/29. Outliers How To Find Them-ksqzOCSAp5U.mp4 8.1 MB
- Part 06-Module 01-Lesson 07_Pandas/12. Pandas 7 V1-ruTYp-twXO0.mp4 8.1 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/10. How The Gaussian Class Works-N-5I0d1zJHI.mp4 8.1 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/38. Bloopers Intro 1 V1-Y1weHponR2Q.mp4 8.0 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.mp4 8.0 MB
- Part 02-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.0 MB
- Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.mp4 8.0 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/18. Matching Encodings-398xRMnhjGk.mp4 8.0 MB
- Part 04-Module 01-Lesson 05_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 8.0 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17b 15022032 V1-N9ytffw5AMg.mp4 8.0 MB
- Part 15-Module 01-Lesson 06_Web Development/25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.mp4 7.9 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.mp4 7.9 MB
- Part 06-Module 01-Lesson 07_Pandas/10. Pandas 6 V1-GS1kj04XQcM.mp4 7.9 MB
- Part 06-Module 01-Lesson 07_Pandas/09. Pandas 5 V1-lClsJnZn_7w.mp4 7.9 MB
- Part 04-Module 01-Lesson 01_Clustering/20. 19 Feature Scaling Solution V1-xddMZP2SQ1U.mp4 7.8 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt3-_HTolKktaC4.mp4 7.8 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 23313044 V1-pcaaBWbe34Y.mp4 7.8 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.mp4 7.7 MB
- Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-resolve-merge-conflict.gif 7.7 MB
- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.mp4 7.7 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/04. Setting Up Hypotheses - Part II-nByvHz77GiA.mp4 7.7 MB
- Part 06-Module 01-Lesson 03_Control Flow/02. Indentation-G8qUNOTHtrM.mp4 7.6 MB
- Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. L2 - Pushing To A Fork-WRgNpr19t48.mp4 7.6 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/06. 02 Writing Modular Code V2-qN6EOyNlSnk.mp4 7.6 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.mp4 7.6 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.mp4 7.6 MB
- Part 03-Module 01-Lesson 03_Training Neural Networks/07. Regularization-ndYnUrx8xvs.mp4 7.6 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/03. Types Of Experiments-7ihDj4M7EiU.mp4 7.6 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.mp4 7.5 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.5 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/21. 15 Making a Package v2-Hj2OBr1CGZM.mp4 7.5 MB
- Part 06-Module 01-Lesson 06_NumPy/04. NumPy 1 V1-EOHW29kDg7w.mp4 7.5 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.mp4 7.5 MB
- Part 07-Module 01-Lesson 03_SQL Aggregations/29. 11 CASE V2-BInXuTY_FzE.mp4 7.5 MB
- Part 07-Module 01-Lesson 02_SQL Joins/14. Other JOINs-4edRxFmWUEw.mp4 7.5 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.mp4 7.5 MB
- Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.mp4 7.5 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.mp4 7.5 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4 7.5 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/21. Conclusions In Hypothesis Testing-I0Mo7hcxahY.mp4 7.5 MB
- Part 01-Module 02-Lesson 01_Setting Up Your Computer/09. Programming Environment Setup-EKxDnCK0NAk.mp4 7.4 MB
- Part 06-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.mp4 7.4 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.mp4 7.4 MB
- Part 06-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.mp4 7.4 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/30. Removing Data-97UTBiybYTs.mp4 7.3 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/25. Multiple Testing Corrections-DuMgeHrkIF0.mp4 7.3 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.mp4 7.3 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/16. What Is A P-value Anyway-eU6pUZjqviA.mp4 7.3 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17b 5451216 V1-lf2Q0AE5esk.mp4 7.3 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.2 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.mp4 7.2 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.2 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.mp4 7.1 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 23242831 V1-WqNi0B_oRuA.mp4 7.1 MB
- Part 01-Module 02-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.mp4 7.1 MB
- Part 15-Module 01-Lesson 06_Web Development/20. 22 Screencast Flask V2-i_U3O-7cymk.mp4 7.1 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.mp4 7.1 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.1 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/03. L2 031 Levels Of Measurement And Types Of Data V6-3Plhn5Q4xIA.mp4 7.1 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.0 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 2 Advantages Of Using Pipelines V1 V2-eT1MS3n8fZ8.mp4 7.0 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 4271048 V1-2On65U7Panw.mp4 7.0 MB
- Part 04-Module 01-Lesson 03_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 12-Module 01-Lesson 12_Hypothesis Testing/26. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.mp4 7.0 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. DataVis L3 03 V2-srRhFrSPdvs.mp4 7.0 MB
- Part 06-Module 01-Lesson 07_Pandas/08. Pandas 4 V1-eMHUn9v9dds.mp4 6.9 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 6.9 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/10. More Personalized Recommendations-9l8mi7i6iW4.mp4 6.9 MB
- Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.mp4 6.8 MB
- Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. DataVis L5C02 V3-bgDNMfG9Gfs.mp4 6.8 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/09. Business And Data Understanding - Part 2-iInjuIgBWIo.mp4 6.8 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.mp4 6.8 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment Pt 2-PYzN1usi7QY.mp4 6.8 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.mp4 6.7 MB
- Part 01-Module 02-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.mp4 6.7 MB
- Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.mp4 6.7 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.mp4 6.7 MB
- Part 15-Module 01-Lesson 06_Web Development/24. Flask Pandas Plotly Part 1-xg7P8MnItdI.mp4 6.7 MB
- Part 02-Module 01-Lesson 08_Model Evaluation Metrics/15. ROC Curve-2Iw5TiGzJI4.mp4 6.7 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.mp4 6.6 MB
- Part 06-Module 01-Lesson 06_NumPy/11. NumPy 6 V1-wtLRuGK0kW4.mp4 6.6 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.6 MB
- Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.mp4 6.6 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.mp4 6.6 MB
- Part 02-Module 01-Lesson 09_Training and Tuning/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.6 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/18. It Is Not Always About ML-ECqflypBU7M.mp4 6.5 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.mp4 6.5 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.mp4 6.5 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4 6.5 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.mp4 6.5 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/11. Design Integrity-y72_fVFtqlY.mp4 6.5 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 31423505 V1-A0uOjClDnW8.mp4 6.5 MB
- Part 02-Module 01-Lesson 10_Finding Donors Project/07. Project 1-PNsxDWtpQTk.mp4 6.4 MB
- Part 03-Module 01-Lesson 03_Training Neural Networks/04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4 6.4 MB
- Part 04-Module 01-Lesson 03_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 08-Module 01-Lesson 03_Univariate Exploration of Data/03. L3 031 Bar Charts V3-ybXcduB6cXA.mp4 6.4 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.mp4 6.4 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/01. Introduction-RVcFzwBXI2M.mp4 6.4 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 0422 V1-J4MOXJhMGGA.mp4 6.3 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.mp4 6.3 MB
- Part 02-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.3 MB
- Part 12-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.mp4 6.3 MB
- Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.mp4 6.3 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.mp4 6.3 MB
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- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/06. Notes On OOP-NcgDIWm6iBA.mp4 6.3 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.mp4 6.3 MB
- Part 01-Module 02-Lesson 01_Setting Up Your Computer/01. Introduction-Yg0gBpTzkMo.mp4 6.2 MB
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- Part 08-Module 01-Lesson 06_Explanatory Visualizations/07. Data Vis L6 C06 V1-qIot9qrvcF8.mp4 6.2 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.2 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.2 MB
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- Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.mp4 6.1 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. L3 071 Pie Charts V3-kSrJGJHTKV8.mp4 6.1 MB
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- Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.mp4 6.0 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/16. Identifying Recommendations-P60qvS_OTMg.mp4 6.0 MB
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- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/07. L2 2 09 Test Driven Development DS V1 V2-M-eskssLcQM.mp4 5.9 MB
- Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.mp4 5.9 MB
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- Part 15-Module 01-Lesson 06_Web Development/01. L4 Intro V2--PGMIIXFCgg.mp4 5.9 MB
- Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.mp4 5.9 MB
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- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/26. Types Of Ratings-fMjqe4sxBlQ.mp4 5.8 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/19. 15 Pipelines And Grid Search V1 V3-HZaOiSxJjCY.mp4 5.7 MB
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- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/20. The Cold Start Problem-DNz7aywJVzA.mp4 5.7 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 5.7 MB
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- Part 06-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.mp4 5.6 MB
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- Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.mp4 5.6 MB
- Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. DataVis L5C08 V2-fq-hakwfpZw.mp4 5.5 MB
- Part 01-Module 03-Lesson 01_What Is Ahead/05. Outro-xj70jX9Moxs.mp4 5.5 MB
- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/06. Outro-xj70jX9Moxs.mp4 5.5 MB
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- Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/06. L1 061 Visualization In Python V1-MFS-1veFC_c.mp4 5.5 MB
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- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/02. History - Statisticians Perspective-zNNouqLGF9E.mp4 5.5 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 0425 V1-vPpX7ITgb3g.mp4 5.5 MB
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- Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.4 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.4 MB
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- Part 02-Module 01-Lesson 03_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.4 MB
- Part 12-Module 01-Lesson 14_Regression/13. Fitting A Regression Line-xQob80zrT3s.mp4 5.4 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/23. Putting It All Together-r5jfD2uKnbQ.mp4 5.4 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.mp4 5.4 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.mp4 5.4 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.3 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/01. Introduction-5DfFaAl1Wmc.mp4 5.3 MB
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- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.3 MB
- Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/02. Disaster Relief Project Preview-DuwYAjqGM3E.mp4 5.3 MB
- Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules-jPGyFgcIvsM.mp4 5.3 MB
- Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.mp4 5.3 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.mp4 5.3 MB
- Part 15-Module 01-Lesson 06_Web Development/18. L4 The Back End V2-Esl0NL63S2c.mp4 5.3 MB
- Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.mp4 5.3 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.mp4 5.3 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. L4 021 Scatterplots And Correlation V2-wqMwTDVT9_Y.mp4 5.3 MB
- Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.mp4 5.3 MB
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- Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.mp4 5.3 MB
- Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.mp4 5.2 MB
- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.mp4 5.2 MB
- Part 04-Module 01-Lesson 04_PCA/08. PCA Properties-1oaaq-0wdB0.mp4 5.2 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.mp4 5.2 MB
- Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.mp4 5.2 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.mp4 5.2 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes Pt 2-yLdXcRXcfPw.mp4 5.2 MB
- Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/13. Early Stopping-taIJZMNwRsI.mp4 5.2 MB
- Part 02-Module 01-Lesson 02_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.2 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. DataVis L3 08 V2-f1we_0dUSXg.mp4 5.2 MB
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- Part 02-Module 01-Lesson 03_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.1 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.1 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/12. Conclusions-yMRRXDKb428.mp4 5.1 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.mp4 5.1 MB
- Part 06-Module 01-Lesson 06_NumPy/09. NumPy 5 V1-vGjI-WTnEbY.mp4 5.1 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.mp4 5.1 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/25. Removing Data - Why Not-w3-5Z5mEzTM.mp4 5.1 MB
- Part 04-Module 01-Lesson 04_PCA/13. 12 Interpret PCA Results V1-ZX6EACfsZbc.mp4 5.1 MB
- Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/01. L1 011 Data Visualization In Data Analysis Intro V3 V3-U1VapEELBfw.mp4 5.1 MB
- Part 07-Module 01-Lesson 01_Basic SQL/11. SELECT FROM Statements-urOYuuav4BY.mp4 5.1 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4 5.1 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/45. The Data Science Process Evaluate And Deploy-sxT43JlH_eM.mp4 5.0 MB
- Part 02-Module 01-Lesson 08_Model Evaluation Metrics/04. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.0 MB
- Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.mp4 5.0 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.mp4 5.0 MB
- Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.mp4 5.0 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/22. 30 Imputing Missing Data V1 V3-A5sOJDj3AKg.mp4 5.0 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 0435 V1-oRhrOShUM6w.mp4 5.0 MB
- Part 10-Module 01-Lesson 04_Add Commits To A Repo/07. Outro-5eyvsMvAPYs.mp4 5.0 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. L3 10 Magic M V1 V3-9dEsv1aNUEE.mp4 5.0 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/03. How Do We Know Our Recs Are Good-D0H_fjJ35CU.mp4 4.9 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/15. Stemming And Lemmatization-7Gjf81u5hmw.mp4 4.9 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 7251010 V1-sVZ5S1nnRf8.mp4 4.9 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.mp4 4.9 MB
- Part 03-Module 01-Lesson 03_Training Neural Networks/05. Model Complexity Graph-NnS0FJyVcDQ.mp4 4.9 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.mp4 4.9 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.mp4 4.9 MB
- Part 15-Module 01-Lesson 06_Web Development/04. The Front End-CspuxLGFM4U.mp4 4.9 MB
- Part 11-Module 01-Lesson 02_Vectors/01. Vectors 1-oPBz-MLVUHk.mp4 4.9 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/11. Captivate Your Audience - First Catch Their Eye-lO8-YKgW7y0.mp4 4.9 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. L3 121 Scales And Transformations V3-PE53ga2bOME.mp4 4.8 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/13. Measuring SImilarity-G_Y6IPmp7Xs.mp4 4.8 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. Error Function-V5kkHldUlVU.mp4 4.8 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 07 Lists And Membership Operators II V3-3Nj-b-ZzqH8.mp4 4.8 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.mp4 4.8 MB
- Part 15-Module 01-Lesson 01_Introduction to Software Engineering/03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.mp4 4.8 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.mp4 4.8 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.mp4 4.8 MB
- Part 02-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 4.8 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/23. Types Of Recommendations-uoXF81AO21E.mp4 4.8 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/03. Fitting A Multiple Linear Regression Model-EZNvBF66_b0.mp4 4.8 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt1-cWB1jQgcQ1g.mp4 4.8 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.mp4 4.7 MB
- Part 04-Module 01-Lesson 04_PCA/01. Introduction-tpFPcxoGxaE.mp4 4.7 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/20. Content Based Recommendations-pnGHpB77Mys.mp4 4.7 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/02. Corporate Messaging Case Study-xnDsUsrF884.mp4 4.7 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/27. Embeddings For Deep Learning-gj8u1KG0H2w.mp4 4.7 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/07. Controlling Variables-pLTneSg2MRY.mp4 4.7 MB
- Part 08-Module 01-Lesson 06_Explanatory Visualizations/04. Same Data Different Stories-jSSnkz3QT5Y.mp4 4.7 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.mp4 4.7 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.mp4 4.7 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.mp4 4.7 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.mp4 4.7 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.mp4 4.7 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. L4 031 Overplotting Transparency And Jitter 1 V4-BGqR-nxgMtg.mp4 4.7 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/14. Parting Words Of Encouragement-sFF_WOnpsXM.mp4 4.7 MB
- Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/11. Parting Words Of Encouragement-sFF_WOnpsXM.mp4 4.7 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/08. Checking Validity-H3H1SZXqDmQ.mp4 4.6 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/27. Removing Data - Other Considerations-xrXk_Tvi0oQ.mp4 4.6 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. L4 111 Faceting V2-oUYRqI6wFGw.mp4 4.6 MB
- Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.mp4 4.6 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.mp4 4.6 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.mp4 4.6 MB
- Part 12-Module 01-Lesson 15_Multiple Linear Regression/26. Recap-VM6GGNC2q8I.mp4 4.6 MB
- Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Action-d3AGtKmHxUk.mp4 4.6 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/26. Conclusion-R5-OYqKk9Ys.mp4 4.6 MB
- Part 07-Module 01-Lesson 03_SQL Aggregations/26. DATE Functions Part II-UPWkDhW4cLI.mp4 4.6 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/13. Using Feature Unions-QmE6CMGar1U.mp4 4.6 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/11. SMART Mnemonic-B0Bnxyu2aKM.mp4 4.5 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt 2-0qcJ_oggdKw.mp4 4.5 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/12. Combining Data From Different Sources-IfMydJvU37M.mp4 4.5 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.mp4 4.5 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/17. Other Important Information-LF-CWF-1mX4.mp4 4.5 MB
- Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.mp4 4.5 MB
- Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.mp4 4.5 MB
- Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.mp4 4.5 MB
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- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/19. Organizing Code Into Modules-AARS10U5bbo.mp4 4.5 MB
- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/04. 06 Unit Tests V1-wb9jggHEvgI.mp4 4.5 MB
- Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.mp4 4.5 MB
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- Part 15-Module 01-Lesson 06_Web Development/08. IDs and Classes-jnfDqdxDbO4.mp4 4.4 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.mp4 4.4 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/13. L3 10 Captivate Your Audience Now What V1-Iy08sZYuqkI.mp4 4.4 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/11. 19 Transform Intro V2 V3-SXp4Qa-rQJg.mp4 4.4 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.mp4 4.4 MB
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- Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.mp4 4.4 MB
- Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.mp4 4.4 MB
- Part 10-Module 01-Lesson 03_Review a Repo's History/07. A Repository's History - Outro-9rUf2HbdAd8.mp4 4.4 MB
- Part 04-Module 01-Lesson 01_Clustering/13. 14 How Does KMeans Work V1-y7yZyyHgyYU.mp4 4.4 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/15. L2 10 Documentation V1 V3-M45B2VbPgjo.mp4 4.4 MB
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- Part 12-Module 01-Lesson 12_Hypothesis Testing/29. Hypothesis Testing Conclusion-nQFchD4XPPs.mp4 4.4 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.mp4 4.4 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. Magic Methods in Code-oDuXThOqans.mp4 4.4 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.mp4 4.4 MB
- Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/12. L5 121 Lesson Summary V1-SOBCduyymkQ.mp4 4.4 MB
- Part 06-Module 01-Lesson 06_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.mp4 4.3 MB
- Part 04-Module 01-Lesson 03_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
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- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.mp4 4.3 MB
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- Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.3 MB
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- Part 06-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.mp4 4.2 MB
- Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.mp4 4.2 MB
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- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.mp4 4.2 MB
- Part 03-Module 01-Lesson 03_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.mp4 4.2 MB
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- Part 12-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.mp4 4.2 MB
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- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.mp4 4.2 MB
- Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.mp4 4.2 MB
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- Part 06-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.mp4 4.0 MB
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- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/12. SVD Practice Takeaways-2er0HUDum7k.mp4 4.0 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/24. Working With Missing Values-mbAgYicmzqE.mp4 4.0 MB
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- Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.mp4 4.0 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.mp4 4.0 MB
- Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/15. Conclusions-3IFF1GzUq0Y.mp4 4.0 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.mp4 4.0 MB
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- Part 03-Module 01-Lesson 03_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.0 MB
- Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.mp4 3.9 MB
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- Part 12-Module 01-Lesson 12_Hypothesis Testing/10. Types Of Errors - Part III-Z-srkCPsdaM.mp4 3.9 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/21. L2 18 Version Control Git Branches V1 V2-C92YcuwjZOs.mp4 3.9 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/13. How Do We Choose Between Hypotheses-JkXTwS-5Daw.mp4 3.9 MB
- Part 08-Module 01-Lesson 06_Explanatory Visualizations/01. L6 011 Intro V1-gLy8qpursJI.mp4 3.9 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/12. Types Of Collaborative Filtering-fZhkWHHP6SM.mp4 3.9 MB
- Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.mp4 3.9 MB
- Part 02-Module 01-Lesson 02_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 3.9 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.mp4 3.9 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.mp4 3.9 MB
- Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.mp4 3.9 MB
- Part 07-Module 01-Lesson 03_SQL Aggregations/25. DATE Functions I-E7Z6GMFVmIY.mp4 3.9 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. L3 011 Intro V3-4BpAF4MYKm8.mp4 3.9 MB
- Part 02-Module 01-Lesson 02_Linear Regression/13. Minimizing Error Functions-RbT2TXN_6tY.mp4 3.8 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4 3.8 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 3.8 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.mp4 3.8 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 3.8 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. A Little More History - A Computer Scientist's Perspective-sVT9nX6HTyU.mp4 3.8 MB
- Part 12-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.mp4 3.8 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/34. Scaling Data-OgjTk3XCUUE.mp4 3.8 MB
- Part 07-Module 01-Lesson 03_SQL Aggregations/02. NULLs-WYUkLKn6XCw.mp4 3.8 MB
- Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.mp4 3.8 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.mp4 3.8 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.mp4 3.8 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/26. GloVe-KK3PMIiIn8o.mp4 3.8 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.mp4 3.8 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/04. L3 Git And Github WalkThrough V1-buMNfXkj9fg.mp4 3.8 MB
- Part 04-Module 01-Lesson 04_PCA/02. Lesson Topics-LBzA08F_r4w.mp4 3.8 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.mp4 3.8 MB
- Part 06-Module 01-Lesson 07_Pandas/04. Pandas 1 V1-iXnYN8cnhzs.mp4 3.8 MB
- Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/07. Outro-ot4fPX1jzOI.mp4 3.8 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.mp4 3.8 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.mp4 3.8 MB
- Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.mp4 3.8 MB
- Part 06-Module 01-Lesson 07_Pandas/05. Pandas 2 V1-B7MuFIwboKU.mp4 3.8 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/19. The Data Science Process Modeling-bzR6HQBn5CA.mp4 3.8 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.8 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Simulating From the Null-sL2yJtHZd8Y.mp4 3.8 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Data Vis L4 C03 V1-0F6ldBC6Nbs.mp4 3.8 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.mp4 3.7 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. Data Vis L4 C13 V1-Z7NjwA6jbjU.mp4 3.7 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. Data Vis L4 C09 V1-OnzWhpgM9Vs.mp4 3.7 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.mp4 3.7 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.mp4 3.7 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.mp4 3.7 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.mp4 3.7 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.mp4 3.7 MB
- Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.mp4 3.7 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.mp4 3.7 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.mp4 3.7 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.7 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.7 MB
- Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.mp4 3.7 MB
- Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.mp4 3.6 MB
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- Part 08-Module 01-Lesson 06_Explanatory Visualizations/06. L6 061 Polishing Plots V3-4TixzVx79uk.mp4 3.6 MB
- Part 15-Module 01-Lesson 06_Web Development/02. L4 Lesson Overview V2-9WQF-CCNdJ8.mp4 3.6 MB
- Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.mp4 3.6 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.mp4 3.6 MB
- Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.mp4 3.6 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4 3.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.mp4 3.6 MB
- Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.mp4 3.6 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/14. Performance Tuning Motivation-aY4_uYWEuoE.mp4 3.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.mp4 3.6 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Data Vis L4 C02 V1-wBDC5AmYgyg.mp4 3.6 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers-TBxUCQdXRjY.mp4 3.6 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.mp4 3.6 MB
- Part 02-Module 01-Lesson 07_Ensemble Methods/08. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4 3.6 MB
- Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 26 Branching Overview-ywcOC6CLG4s.mp4 3.6 MB
- Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.mp4 3.6 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.5 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/16. 04 Inline Comments V1--G6yg3Xhl8I.mp4 3.5 MB
- Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.mp4 3.5 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.mp4 3.5 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. Inheritance-1gsrxUwPI40.mp4 3.5 MB
- Part 06-Module 01-Lesson 07_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.mp4 3.5 MB
- Part 10-Module 01-Lesson 01_What is Version Control/06. Onward-iXbMaTwfIJI.mp4 3.5 MB
- Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.mp4 3.5 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/27. Dummy Variables-bgxBUvPpKQQ.mp4 3.5 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.mp4 3.5 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 0424 V1-x-End5px36M.mp4 3.5 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.mp4 3.5 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/29. Conclusion-zX5jZH2y8d8.mp4 3.5 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/02. Identifying Recommendation Engines-KwegrgvV-V4.mp4 3.5 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.mp4 3.5 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/16. Inheritance Gaussian Class-XS4LQn1VA3U.mp4 3.5 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/18. Feature Extraction-UgENzCmfFWE.mp4 3.5 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.5 MB
- Part 08-Module 01-Lesson 06_Explanatory Visualizations/09. L6 131 Lesson Summary V1-t6ss31RZF34.mp4 3.5 MB
- Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.mp4 3.5 MB
- Part 02-Module 01-Lesson 09_Training and Tuning/08. Grid Search SC V1-zDw-ZGiHW5I.mp4 3.4 MB
- Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.mp4 3.4 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-O-4qRh74rkI.mp4 3.4 MB
- Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.mp4 3.4 MB
- Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.mp4 3.4 MB
- Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.mp4 3.4 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/17. Ud206 022 Shell Workshop Outro-68twTPXPrx0.mp4 3.4 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/01. Natural Language Processing-UQBxJzoCp-I.mp4 3.4 MB
- Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.mp4 3.4 MB
- Part 02-Module 01-Lesson 07_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4 3.4 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.mp4 3.4 MB
- Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.mp4 3.4 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.mp4 3.4 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.mp4 3.4 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. L4 011 Intro V2-JzvJIWG8Rk4.mp4 3.4 MB
- Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression-Metrics-906P4BPnl9A.mp4 3.4 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.mp4 3.3 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/04. Types Of Sampling-GF_eQqNoarI.mp4 3.3 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. L4 061 Violin Plots 2 V3-0hr61L-LZyM.mp4 3.3 MB
- Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.mp4 3.3 MB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.mp4 3.3 MB
- Part 12-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.mp4 3.3 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.3 MB
- Part 15-Module 01-Lesson 06_Web Development/03. L4 Components Of A Web App V4-2aJf5sO2ox4.mp4 3.3 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.mp4 3.3 MB
- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.mp4 3.3 MB
- Part 04-Module 01-Lesson 01_Clustering/04. 04 KMeans Use Cases 1 1 V2-25paySwVdAA.mp4 3.3 MB
- Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Adding A Commit On GitHub-UBYxcTg6VLU.mp4 3.3 MB
- Part 02-Module 01-Lesson 02_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.3 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.mp4 3.3 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Data Vis L4 C04 V1-O6ElT4IFXc0.mp4 3.2 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.mp4 3.2 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/20. 28 Missing Data Causes V1 V2-zlw8ESS6Q88.mp4 3.2 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/01. Intro-svCesgAQ46Q.mp4 3.2 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.mp4 3.2 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.mp4 3.2 MB
- Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/01. Intro-j5RmK0UHOTY.mp4 3.2 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.2 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization-4Ieotbeh4u8.mp4 3.2 MB
- Part 04-Module 01-Lesson 04_PCA/17. When to Use PCA-arSP83-CM6w.mp4 3.2 MB
- Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/04. Practical Significance-eJ3idt3AJ7E.mp4 3.2 MB
- Part 12-Module 01-Lesson 14_Regression/21. Recap-DzMi27LI5l4.mp4 3.2 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/23. 24 Conclusion V1 V2-Jq6pj_uKDmY.mp4 3.2 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.2 MB
- Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. L5 081 Plot Matrices V3-2wY-euTIE5g.mp4 3.2 MB
- Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.mp4 3.2 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.mp4 3.2 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.mp4 3.2 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.mp4 3.2 MB
- Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.mp4 3.2 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Data Vis L4 C11 V1-3Ls6w8Cd8n4.mp4 3.2 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.mp4 3.1 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/06. Normalization-eOV2UUY8vtM.mp4 3.1 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/14. Ud206 018 P12 Startup Files (.bash_profile)--zF-XebfzBE.mp4 3.1 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.mp4 3.1 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4 3.1 MB
- Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.mp4 3.1 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/12. Ud206 016 Shell P10 - Searching And Pipes-AWpVScp9z4s.mp4 3.1 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/12. 12 Pipelines And Feature Unions V1 V3-zduxy0g23L0.mp4 3.1 MB
- Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.mp4 3.1 MB
- Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. DataVis L5C09 V1-xlZ9AMV6VUE.mp4 3.1 MB
- Part 15-Module 01-Lesson 06_Web Development/32. L4 Outro V2-8MyuJx5yu38.mp4 3.1 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/15. Captivate Your Audience - End With A Call To Action-EajX2NbHJ6w.mp4 3.1 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/06. Types Of Errors - Part I-aw6GMxIvENc.mp4 3.1 MB
- Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4 3.1 MB
- Part 08-Module 01-Lesson 06_Explanatory Visualizations/08. L6 10 V1 V6-LoYT4NMSPGk.mp4 3.1 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.mp4 3.1 MB
- Part 16-Module 01-Lesson 02_ETL Pipelines/39. 47 Load V1 V1-Us1hWDaabxo.mp4 3.1 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.mp4 3.1 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/20. Using Grid Search-iTL43Jk9_bQ.mp4 3.1 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.mp4 3.0 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/23. L2 18 Version Control Merging Branches On A Team V1 V2-36DOnNzvT4A.mp4 3.0 MB
- Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.mp4 3.0 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.mp4 3.0 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/01. Lesson Introduction-rw3YaQ2CTNQ.mp4 3.0 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.0 MB
- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/08. L2 2 11 Logging V2-9qKQdRoIMbU.mp4 3.0 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.mp4 3.0 MB
- Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Data Vis L4 C06 V2-f8Kh4PByiEA.mp4 3.0 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.0 MB
- Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.mp4 3.0 MB
- Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. L3 - Include Upstream Changes-VvoC6hN6FjU.mp4 3.0 MB
- Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.mp4 3.0 MB
- Part 08-Module 01-Lesson 02_Design of Visualizations/18. L2 181 Lesson Summary HDmp4 V3-kKEeBDs4HuM.mp4 3.0 MB
- Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/22. Virtual Environments-f7rzxUiHOJ0.mp4 3.0 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/27. Goals Of Recommendation Systems-WzelOlFeDmU.mp4 3.0 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/25. Word2Vec-7jjappzGRe0.mp4 3.0 MB
- Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.mp4 3.0 MB
- Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4 3.0 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/07. Latent Factors-jZz7tFEF2Dc.mp4 3.0 MB
- Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.mp4 3.0 MB
- Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.mp4 3.0 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.mp4 3.0 MB
- Part 04-Module 01-Lesson 04_PCA/07. Dimensionality Reduction-mANti9veGtc.mp4 3.0 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.mp4 3.0 MB
- Part 15-Module 01-Lesson 06_Web Development/26. Flask Pandas Plotly Part3-e8owK5zk-g8.mp4 3.0 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.mp4 2.9 MB
- Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.mp4 2.9 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.mp4 2.9 MB
- Part 15-Module 01-Lesson 06_Web Development/07. Div and Span-cbKA_dvthcY.mp4 2.9 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.mp4 2.9 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4 2.9 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment-fH_xF5_SDCE.mp4 2.9 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/19. Conclusion-_ATzG6khLdk.mp4 2.9 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.mp4 2.9 MB
- Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. L3 081 Histograms V2-RLez9L0htGQ.mp4 2.9 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.mp4 2.9 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.mp4 2.9 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.9 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.9 MB
- Part 16-Module 01-Lesson 04_Machine Learning Pipelines/01. 01 Intro V1 V3-Zl_es7xtSqk.mp4 2.9 MB
- Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. INTRODUÇÃO AO PROJETO PRINCIPAL V2---9IFCNBM6Y.mp4 2.9 MB
- Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.mp4 2.9 MB
- Part 02-Module 01-Lesson 07_Ensemble Methods/09. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4 2.9 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.mp4 2.8 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.mp4 2.8 MB
- Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.mp4 2.8 MB
- Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/25. L2 21 Conclusion V1 V1-anPnokWZOZQ.mp4 2.8 MB
- Part 02-Module 01-Lesson 02_Linear Regression/21. Closed Form Solution-G3fRVgLa5gI.mp4 2.8 MB
- Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.mp4 2.8 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/06. Up and Running On Medium-0QzbxjAcMq0.mp4 2.8 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 2.8 MB
- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.mp4 2.8 MB
- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.mp4 2.8 MB
- Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.mp4 2.8 MB
- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.mp4 2.8 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.mp4 2.8 MB
- Part 12-Module 01-Lesson 14_Regression/02. Introduction to Machine Learning-pLcFPPI1L-0.mp4 2.8 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/04. Types Of Machine Learning - Supervised-Jn3xugBvs2U.mp4 2.8 MB
- Part 08-Module 01-Lesson 07_Visualization Case Study/07. L7 0F1 Congrats V3-LF-obnL7CI0.mp4 2.8 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.mp4 2.8 MB
- Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.mp4 2.8 MB
- Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.mp4 2.8 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.mp4 2.8 MB
- Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.mp4 2.8 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.mp4 2.8 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.mp4 2.8 MB
- Part 14-Module 01-Lesson 02_Communicating to Stakeholders/10. Three Steps To Captivate Your Audience-BWS3oQYS-c4.mp4 2.7 MB
- Part 02-Module 01-Lesson 08_Model Evaluation Metrics/01. 01 Intro-4C4PuJANIdE.mp4 2.7 MB
- Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.mp4 2.7 MB
- Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. L5 051 Faceting In Two Directions V3-lz5dcoTcV2o.mp4 2.7 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.mp4 2.7 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.7 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.7 MB
- Part 02-Module 01-Lesson 08_Model Evaluation Metrics/14. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4 2.7 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.mp4 2.7 MB
- Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.mp4 2.7 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/10. The Data Science Process Gathering And Wrangling-GvyfIiJUXWg.mp4 2.7 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.mp4 2.7 MB
- Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions--UvpQV1qmiE.mp4 2.7 MB
- Part 02-Module 01-Lesson 07_Ensemble Methods/10. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4 2.7 MB
- Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/07. Knowledge Based Recommendations-C_vU1tjQHZI.mp4 2.6 MB
- Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.mp4 2.6 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.6 MB
- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.mp4 2.6 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.mp4 2.6 MB
- Part 17-Module 02-Lesson 03_AB Testing Case Study/01. Intro-28mN6RvGXDM.mp4 2.6 MB
- Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.mp4 2.6 MB
- Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.mp4 2.6 MB
- Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.mp4 2.6 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4 2.6 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.6 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.mp4 2.6 MB
- Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.mp4 2.6 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.mp4 2.6 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.mp4 2.6 MB
- Part 02-Module 01-Lesson 02_Linear Regression/10. Mean Absolute Error-vLKiY0Ehors.mp4 2.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.mp4 2.6 MB
- Part 01-Module 02-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.mp4 2.6 MB
- Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.mp4 2.6 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.mp4 2.5 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.mp4 2.5 MB
- Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.mp4 2.5 MB
- Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.mp4 2.5 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/20. 02 TF-IDF-LYYWIrWbBq4.mp4 2.5 MB
- Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.mp4 2.5 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.mp4 2.5 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.mp4 2.5 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.mp4 2.5 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.mp4 2.5 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.mp4 2.5 MB
- Part 02-Module 01-Lesson 07_Ensemble Methods/06. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4 2.5 MB
- Part 12-Module 01-Lesson 12_Hypothesis Testing/13. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.mp4 2.5 MB
- Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.mp4 2.5 MB
- Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.mp4 2.5 MB
- Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.mp4 2.5 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.mp4 2.4 MB
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- Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.4 MB
- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.mp4 2.4 MB
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- Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.mp4 2.3 MB
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- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.mp4 2.2 MB
- Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.mp4 2.2 MB
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- Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.mp4 2.2 MB
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- Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.mp4 2.1 MB
- Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.mp4 2.1 MB
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- Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.mp4 2.0 MB
- Part 07-Module 01-Lesson 01_Basic SQL/06. Why Businesses Choose Databases-j4ey7--h9r8.mp4 2.0 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.mp4 2.0 MB
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- Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.mp4 2.0 MB
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- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 2.0 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.mp4 2.0 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/10. Stop Word Removal-WAU_Ij0GJbw.mp4 2.0 MB
- Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 1-SNFHYbJvlZU.mp4 1.9 MB
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- Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.mp4 1.9 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.mp4 1.9 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 1.9 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.mp4 1.9 MB
- Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.mp4 1.9 MB
- Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.mp4 1.9 MB
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- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.mp4 1.9 MB
- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.mp4 1.9 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.mp4 1.9 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 1.9 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.mp4 1.9 MB
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- Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.mp4 1.8 MB
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- Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.mp4 1.8 MB
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- Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4 1.8 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.mp4 1.8 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.mp4 1.8 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.mp4 1.8 MB
- Part 01-Module 02-Lesson 01_Setting Up Your Computer/20. L2 02 Outro REPLACEMENT-W-6Se0G_FVE.mp4 1.8 MB
- Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/17. Funk SVD Review-nc3GMIrISHE.mp4 1.8 MB
- Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-6LO6I5M18PQ.mp4 1.8 MB
- Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.mp4 1.8 MB
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- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/07. Categorical Ordinal Nominal Data-k5bLaPGY2Vw.mp4 1.7 MB
- Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/img/screen-shot-2018-05-29-at-4.06.53-pm.png 1.7 MB
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- Part 03-Module 01-Lesson 03_Training Neural Networks/16. Error Functions Around the World-34AAcTECu2A.mp4 1.7 MB
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- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.7 MB
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- Part 04-Module 01-Lesson 04_PCA/04. Latent Features-kYLcVgpEwGs.mp4 1.7 MB
- Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.mp4 1.7 MB
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- Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.mp4 1.6 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.mp4 1.6 MB
- Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4 1.6 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.mp4 1.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.mp4 1.6 MB
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- Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.mp4 1.6 MB
- Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.mp4 1.6 MB
- Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes Pt 1-HPmMEkbT2uE.mp4 1.6 MB
- Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4 1.6 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.mp4 1.6 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.mp4 1.6 MB
- Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.mp4 1.6 MB
- Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.mp4 1.6 MB
- Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4 1.6 MB
- Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.mp4 1.5 MB
- Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.mp4 1.5 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.5 MB
- Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.mp4 1.5 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.mp4 1.5 MB
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- Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.mp4 1.5 MB
- Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.5 MB
- Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.mp4 1.5 MB
- Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.mp4 1.4 MB
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- Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.mp4 1.4 MB
- Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4 1.4 MB
- Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.mp4 1.4 MB
- Part 08-Module 01-Lesson 07_Visualization Case Study/01. L7 011 Intro V1-Virihwp36do.mp4 1.4 MB
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- Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/codecogseqn-2.png 2.3 KB
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- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.pt-BR.vtt 2.3 KB
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- Part 13-Module 01-Lesson 02_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.en.vtt 2.3 KB
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- Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.ar.vtt 2.3 KB
- Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.en.vtt 2.3 KB
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