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Part 12-Module 01-Lesson 05_Interview Practice/06. Design a Spam Classifier-qRv1wrtgsmM.mp4 118.9 MBPart 12-Module 01-Lesson 05_Interview Practice/05. Maximum Difference in an Integer Array-i3RTW83wI1Q.mp4 112.4 MBPart 12-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4 110.1 MBPart 12-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4 109.7 MBPart 11-Module 01-Lesson 06_Datasets and Questions/01. Introduction-TdopVWltgqM.mp4 92.2 MBPart 06-Module 01-Lesson 03_Analyzing Data/19. Geospatial Indexes-iGnSNfzaLf4.mp4 71.2 MBPart 06-Module 01-Lesson 03_Analyzing Data/11. Unwind Operator-6wBjB3Ca5H4.mp4 61.3 MBPart 12-Module 01-Lesson 05_Interview Practice/03. Free Throw Probability-Zyq0FQ0XO3o.mp4 59.4 MBPart 14-Module 01-Lesson 05_Analyzing Results/09. Single Metric Example-LCZG4wlEFXE.mp4 56.3 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/29. Lessons Learned-_dnp6ItYOdA.mp4 55.4 MBPart 13-Module 01-Lesson 08_Animation and Interaction/49. The Future of Data Visualizations--CWbVBYBYWU.mp4 55.2 MBPart 12-Module 01-Lesson 05_Interview Practice/04. Query a SQL database-UVSFLWdAKl4.mp4 53.6 MBPart 13-Module 01-Lesson 03_Problem Set Visualization Fundamentals/01. Data Visualization Resources-OYl-saB2vWE.mp4 52.9 MBPart 05-Module 01-Lesson 01_Data Extraction Fundamentals/15. JSON Playground-la_rboWWjQI.mp4 52.4 MBPart 12-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4 52.2 MBPart 09-Module 01-Lesson 07_Explore Many Variables/16. Sean's NFL Fan Sentiment Study-ahaxt6UKxQw.mp4 52.0 MBPart 14-Module 01-Lesson 05_Analyzing Results/16. Analyzing Multiple Metrics-mbHnQ_EdrZ8.mp4 52.0 MBPart 04-Module 03-Lesson 01_Investigate a Dataset/05. Investigate A Dataset Project Walkthrough Final-OtDZCYxbHB4.mp4 51.8 MBPart 14-Module 01-Lesson 04_Designing an Experiment/23. Learning Effects-nFpIDWPUDm8.mp4 51.4 MBPart 05-Module 01-Lesson 01_Data Extraction Fundamentals/09. Using CSV Module--S4vc6tvxoI.mp4 51.2 MBPart 06-Module 01-Lesson 03_Analyzing Data/17. Indexes-tSgPhxZdhLk.mp4 50.4 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/09. Other Techniques, Part 2-siuolDaB4eU.mp4 49.4 MBPart 03-Module 01-Lesson 11_L4 Variability/32. SD Social Networkers-cy8BwWl36Q4.mp4 47.4 MBPart 03-Module 01-Lesson 07_Google Spreadsheet Tutorial/01. Tutorial-c3yK3OhuNvo.mp4 47.3 MBPart 06-Module 01-Lesson 03_Analyzing Data/03. Example of Aggregation Framework-pOJAW4jTjr0.mp4 46.9 MBPart 06-Module 01-Lesson 03_Analyzing Data/18. Using Indexes-vL9FcO_9dCA.mp4 46.8 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/22. Variability Example-gs4qKb-0Lfs.mp4 46.7 MBPart 12-Module 01-Lesson 05_Interview Practice/02. Describe your latest data project-cXluuqCVg18.mp4 44.2 MBPart 05-Module 01-Lesson 05_Data Quality/08. Example Using our Blueprint-G3RjHqqQSgE.mp4 43.9 MBPart 05-Module 01-Lesson 05_Data Quality/18. Auditing Uniformity-cF0AEW4SIUM.mp4 43.0 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/12. Metric Definition and Data Capture-qtVm_stSHuM.mp4 42.9 MBPart 06-Module 01-Lesson 03_Analyzing Data/13. Group Accumulation Operators-cleAY1-Jboc.mp4 42.8 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/13. Metric Definition Example-xOSypbjALh4.mp4 42.8 MBPart 12-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4 42.7 MBPart 05-Module 01-Lesson 03_Data in More Complex Formats/07. Parsing XML-SIsG0HYYwY4.mp4 42.4 MBPart 14-Module 01-Lesson 05_Analyzing Results/07. Sanity Checking Wrapup-rCAYdZk5O2E.mp4 42.2 MBPart 05-Module 02-Lesson 07_Case study OpenStreetMap Data [SQL]/02. Familiarize Yourself with the Dataset-JyX6j00Q2Cg.mp4 41.9 MBPart 06-Module 01-Lesson 05_Case Study OpenStreetMap Data/02. Familiarize Yourself with the Dataset-JyX6j00Q2Cg.mp4 41.9 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/14. Filtering and Segmenting-DETCNMqnnKw.mp4 40.6 MBPart 06-Module 01-Lesson 05_Case Study OpenStreetMap Data/10. Reviewing our Street Name Audit-pH3VusOq9tc.mp4 40.2 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/30. Lessons Learned, Part 2-1JRk6dgQhkE.mp4 40.2 MBPart 06-Module 01-Lesson 01_Working with MongoDB/25. Dot Notation-OcPW6xp8DfE.mp4 39.7 MBPart 06-Module 01-Lesson 03_Analyzing Data/09. Project Operator-_kupbxrZBds.mp4 39.3 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/24. Empirical Variability-Wzs1KFldiUE.mp4 38.9 MBPart 14-Module 01-Lesson 04_Designing an Experiment/03. Unit of Diversion Example-W4Xno_vJvZM.mp4 38.5 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/16. Summary Metrics-ghMD3AfjLZc.mp4 38.0 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/03. Metric Definition Overview, Part 2-0DyLzS2ux7g.mp4 37.5 MBPart 14-Module 01-Lesson 04_Designing an Experiment/11. Target Population-HO1Doq8PiCI.mp4 37.4 MBPart 06-Module 01-Lesson 03_Analyzing Data/15. Multiple Stages Using a Given Operator-0TV8kTw9-HM.mp4 37.0 MBPart 14-Module 01-Lesson 04_Designing an Experiment/04. Consistency of Diversion-im8-LLU5J_Y.mp4 36.7 MBPart 11-Module 01-Lesson 06_Datasets and Questions/03. Accuracy vs. Training Set Size-9w1Yi5nMNgw.mp4 36.5 MBPart 09-Module 01-Lesson 01_What is EDA/08. Aude Explores Coordinated Migration-7ihp6ofAJG8.mp4 36.4 MBPart 05-Module 02-Lesson 07_Case study OpenStreetMap Data [SQL]/08. Auditing Street Names-c0IcUiF_kXs.mp4 36.4 MBPart 06-Module 01-Lesson 05_Case Study OpenStreetMap Data/09. Auditing Street Names-c0IcUiF_kXs.mp4 36.4 MBPart 14-Module 01-Lesson 01_Overview of AB Testing/03. Intro to AB Testing-ASQubLvGuxY.mp4 36.1 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/25. Empirical Variability Sanity Checking-6vxdVWIA6dI.mp4 36.0 MBPart 05-Module 01-Lesson 03_Data in More Complex Formats/03. XML Design Principles-eQpSC0IFhv0.mp4 36.0 MBPart 14-Module 01-Lesson 05_Analyzing Results/18. Gotchas Changes Over Time-P7Hso-EoWvA.mp4 35.8 MBPart 02-Module 01-Lesson 01_Intro to Research Methods/04. Define Constructs-J2kY_Xf7i1E.mp4 35.6 MBPart 03-Module 01-Lesson 01_L1 Intro to Research Methods/04. Define Constructs-J2kY_Xf7i1E.mp4 35.6 MBPart 13-Module 01-Lesson 01_Visualization Fundamentals/26. Up and Down the Visualization Spectrum-NdVf2bY8OnM.mp4 35.3 MBPart 14-Module 01-Lesson 04_Designing an Experiment/24. Lessons Learned-GonjL0GbNiw.mp4 34.9 MBPart 12-Module 01-Lesson 05_Interview Practice/06. Design a Spam Classifier-Uy89Ff49pRc.mp4 34.9 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/05. Refining the Customer Funnel-L2NEk4jvlcY.mp4 34.9 MBPart 05-Module 02-Lesson 07_Case study OpenStreetMap Data [SQL]/05. Iterating through Ways Tags-GYhOEJ8EXkg.mp4 34.3 MBPart 06-Module 01-Lesson 05_Case Study OpenStreetMap Data/06. Iterating through Ways Tags-GYhOEJ8EXkg.mp4 34.3 MBPart 10-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 34.1 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/18. Sensitivity and Robustness-T2QI3p0yDNk.mp4 34.1 MBPart 11-Module 01-Lesson 13_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4 34.0 MBPart 03-Module 01-Lesson 30_L11 t-Tests, Part 3/02. Standard Error-GTsnxTCjmcM.mp4 33.8 MBPart 05-Module 01-Lesson 05_Data Quality/10. Wikipedia Infobox Dataset-hpyNAYJODqw.mp4 33.7 MBPart 12-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4 33.2 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/08. Defining Metrics Other Techniques-gEO8il9GU3M.mp4 33.1 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/19. Sensitivity and Robustness Example-_I7m7Y4hBOs.mp4 32.7 MBPart 12-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4 32.6 MBPart 14-Module 01-Lesson 04_Designing an Experiment/20. Duration vs. Exposure-MsByTo7FRgs.mp4 32.4 MBPart 14-Module 01-Lesson 05_Analyzing Results/06. Checking Invariants, Part 2-05jVh4kUbHU.mp4 32.3 MBPart 11-Module 01-Lesson 14_Validation/10. Practical Advice for K-Fold in sklearn-COVRSk0GDEE.mp4 32.0 MBPart 05-Module 01-Lesson 01_Data Extraction Fundamentals/12. Intro to JSON-Fi7RjhBLobY.mp4 30.3 MBPart 13-Module 01-Lesson 07_Narrative Structures/07. How to Lie with Bar Charts-75c7a4R4pZ0.mp4 30.3 MBPart 06-Module 01-Lesson 01_Working with MongoDB/08. Intro to PyMongo-h6M4x9aXx5g.mp4 30.2 MBPart 13-Module 01-Lesson 07_Narrative Structures/25. Demystifying D3-y7QK8XMHyDQ.mp4 29.9 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/02. Metric Definition Overview-pOxyL1lwBIw.mp4 29.8 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/26. Empirical Confidence Intervals-ca8GQmr7B8g.mp4 29.7 MBPart 05-Module 01-Lesson 05_Data Quality/11. Auditing a Cross-Field Constraint-Y7TNXALXut8.mp4 29.7 MBPart 05-Module 01-Lesson 01_Data Extraction Fundamentals/10. Intro to XLRD-TcWtpvLSoG0.mp4 29.0 MBPart 03-Module 01-Lesson 30_L11 t-Tests, Part 3/01. Independent Samples-K25Xtqr2WvI.mp4 29.0 MBPart 13-Module 01-Lesson 06_Problem Set Chart Types and Axes/13. Scott's Advice to Break into Data Vis-v5ywQMRCxMg.mp4 28.9 MBPart 06-Module 01-Lesson 01_Working with MongoDB/29. Removing Documents-oFWNOvxuIRQ.mp4 28.8 MBPart 14-Module 01-Lesson 05_Analyzing Results/15. Multiple Metrics Example 3-XByoblkCgfE.mp4 28.8 MBPart 14-Module 01-Lesson 05_Analyzing Results/10. Single Metric Gotchas-WrfWFLiFeMU.mp4 28.8 MBPart 05-Module 01-Lesson 05_Data Quality/14. Auditing Accuracy 2-5xY3sCBHkOk.mp4 28.6 MBPart 06-Module 01-Lesson 03_Analyzing Data/04. The Aggregation Pipeline-0sl_mbV-fho.mp4 28.1 MBPart 14-Module 01-Lesson 01_Overview of AB Testing/07. History of AB Testing-NNiKGao0xnk.mp4 28.0 MBPart 03-Module 01-Lesson 08_L3 Central Tendency/29. Formula for Location of Median-lNYLU1b4qoM.mp4 27.9 MBPart 14-Module 01-Lesson 04_Designing an Experiment/10. Inter- vs. Intra-User Experiments-EGE8ddtWIeo.mp4 27.4 MBPart 14-Module 01-Lesson 05_Analyzing Results/09. Single Metric Example--QwMdwqUq9c.mp4 27.2 MBPart 06-Module 01-Lesson 01_Working with MongoDB/27. Set Unset-bgOky14w77Q.mp4 27.2 MBPart 03-Module 01-Lesson 18_L7 Sampling Distributions/22. Simulation Applet-yu4mdXybrts.mp4 27.0 MBPart 05-Module 02-Lesson 07_Case study OpenStreetMap Data [SQL]/03. Iterative Parsing-pxUvIP4PQt0.mp4 27.0 MBPart 06-Module 01-Lesson 05_Case Study OpenStreetMap Data/03. Iterative Parsing-pxUvIP4PQt0.mp4 27.0 MBPart 02-Module 01-Lesson 01_Intro to Research Methods/23. McDonald's-29Vf6PytVbA.mp4 26.7 MBPart 03-Module 01-Lesson 01_L1 Intro to Research Methods/23. McDonald's-29Vf6PytVbA.mp4 26.7 MBPart 16-Module 02-Lesson 01_The Data Analysis Process/05. DAP Overview-qdV4sifMmWI.mp4 26.7 MBPart 13-Module 01-Lesson 07_Narrative Structures/28. Joins in D3-rDjOINfKftY.mp4 26.6 MBPart 05-Module 01-Lesson 05_Data Quality/15. Auditing Completeness-PMbcv9AYQT4.mp4 26.6 MBPart 14-Module 01-Lesson 05_Analyzing Results/17. Drawing Conclusions-CZQ-nZyUG7g.mp4 26.5 MBPart 11-Module 01-Lesson 02_Naive Bayes/01. ML in The Google Self-Driving Car-lL16AQItG1g.mp4 26.3 MBPart 07-Module 01-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 26.3 MBPart 13-Module 01-Lesson 02_D3 Building Blocks/28. Server Requests and D3-Lx9dhgMZO1s.mp4 26.2 MBPart 18-Module 03-Lesson 01_Assessing Data/15. Quality Programatic Assessment 1-l-FhDkQRclA.mp4 26.1 MBPart 05-Module 02-Lesson 07_Case study OpenStreetMap Data [SQL]/09. Reviewing our Street Name Audit-eIvm5rEMZGY.mp4 25.9 MBPart 12-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 25.9 MBPart 03-Module 01-Lesson 28_L10b t-Tests, Part 2/08. Report Results-xNbzHOnSx5Q.mp4 25.8 MBPart 05-Module 02-Lesson 07_Case study OpenStreetMap Data [SQL]/04. Ways in the Open Street Map XML-1OwIbj9dcLo.mp4 25.7 MBPart 06-Module 01-Lesson 05_Case Study OpenStreetMap Data/05. Ways in the Open Street Map XML-1OwIbj9dcLo.mp4 25.7 MBPart 13-Module 01-Lesson 08_Animation and Interaction/02. Case Study US Unemployment-R6Nk8NsEzUA.mp4 25.1 MBPart 21-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.mp4 24.9 MBPart 06-Module 01-Lesson 01_Working with MongoDB/20. Regex Operator-MbJfmI7kzQI.mp4 24.7 MBPart 05-Module 01-Lesson 03_Data in More Complex Formats/05. Fundamentals of XML-Ti8K9FmNW1w.mp4 24.6 MBPart 05-Module 01-Lesson 03_Data in More Complex Formats/15. Extracting Entities-aJD8WJa3-9A.mp4 24.5 MBPart 11-Module 01-Lesson 09_Clustering/12. Sklearn-3zHUAXcoZ7c.mp4 24.4 MBPart 14-Module 01-Lesson 04_Designing an Experiment/08. Unit of Analysis vs. Unit of Diversion-rzim0wu_0kM.mp4 24.2 MBPart 13-Module 01-Lesson 07_Narrative Structures/13. Errors in Data Collection-xcYDjXpYbs0.mp4 24.2 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/11. Other Techniques Example-z1UX9i9_piQ.mp4 24.1 MBPart 13-Module 01-Lesson 08_Animation and Interaction/46. World Cup Code and Narrative Recap-lnHEvGeech0.mp4 24.1 MBPart 17-Module 01-Lesson 16_Logistic Regression/29. Model Diagnostics In Python-1Z4eorbfOOc.mp4 23.9 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/15. Filtering and Segmenting Example-WgM3cBEtkqg.mp4 23.4 MBPart 09-Module 01-Lesson 09_Diamonds Price Predictions/05. Frances Gerety-GXT_vXBA0vQ.mp4 23.2 MBPart 17-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.mp4 23.1 MBPart 06-Module 01-Lesson 03_Analyzing Data/06. Aggregation Operators 1-mRNyMEoCoY0.mp4 23.1 MBPart 17-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.mp4 23.0 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/15. Filtering and Segmenting Example-XTS6m5_3fdU.mp4 23.0 MBPart 11-Module 01-Lesson 12_Feature Selection/14. Balancing Error with Number of Features-IwiIFMcDwoA.mp4 22.9 MBPart 07-Module 01-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 22.8 MBPart 21-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.mp4 22.7 MBPart 06-Module 01-Lesson 01_Working with MongoDB/28. Multi Update-tuXc0Ycy_yM.mp4 22.6 MBPart 03-Module 01-Lesson 28_L10b t-Tests, Part 2/04. Statistical Significance-kpdwHL54eys.mp4 22.5 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/17. Summary Metrics Example--DqU-ARfQr4.mp4 22.5 MBPart 14-Module 01-Lesson 05_Analyzing Results/13. Multiple Metrics Example-60IGcSho56o.mp4 22.4 MBPart 14-Module 01-Lesson 05_Analyzing Results/19. Lessons Learned-5JjCVTc0FGk.mp4 22.2 MBPart 03-Module 01-Lesson 08_L3 Central Tendency/15. Properties of the Mean-AqlvTMZg6HY.mp4 22.1 MBPart 06-Module 01-Lesson 01_Working with MongoDB/06. Flexible Schema-6a-YgbrEACk.mp4 22.1 MBPart 13-Module 01-Lesson 01_Visualization Fundamentals/07. Design Code Tell-qsWz7r8muII.mp4 22.1 MBPart 03-Module 01-Lesson 08_L3 Central Tendency/10. More o' Mode!-74mudn321tA.mp4 22.0 MBPart 09-Module 01-Lesson 01_What is EDA/03. Why learn EDA-YM68DrqJw1I.mp4 22.0 MBPart 21-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.mp4 22.0 MBPart 14-Module 01-Lesson 05_Analyzing Results/02. Sanity Checks-OVnlC_T3EpI.mp4 22.0 MBPart 03-Module 01-Lesson 18_L7 Sampling Distributions/26. Klout-3q0oPuWxFpA.mp4 21.9 MBPart 11-Module 01-Lesson 13_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4 21.9 MBPart 11-Module 01-Lesson 13_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4 21.8 MBPart 12-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 21.7 MBPart 09-Module 01-Lesson 05_Explore Two Variables/10. Overlaying Summaries with Raw Data-O9d9dKyHtIc.mp4 21.7 MBPart 03-Module 01-Lesson 16_L6 Normal Distribution/15. SD of Karma Points per Post-uCRzrlaA8mg.mp4 21.6 MBPart 10-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 21.6 MBPart 03-Module 01-Lesson 16_L6 Normal Distribution/19. Between 10 and 16-4QIEdzv6g6w.mp4 21.6 MBPart 03-Module 01-Lesson 20_L8 Estimation/23. CI Bounds-6MdZ5NUfen0.mp4 21.6 MBPart 13-Module 01-Lesson 02_D3 Building Blocks/20. How Scales Work-4zqm8dz7Z-k.mp4 21.5 MBPart 03-Module 01-Lesson 23_L9 Hypothesis Testing/06. Critical Regions-UF3japjNx98.mp4 21.4 MBPart 13-Module 01-Lesson 02_D3 Building Blocks/31. Layout and Scales-BQWN9ImjUFE.mp4 21.3 MBPart 14-Module 01-Lesson 04_Designing an Experiment/18. How to Decrease Experiment Size-MLzRdgcFYQQ.mp4 21.3 MBPart 12-Module 01-Lesson 05_Interview Practice/07. Jimmy's Analysis of the Interview-wg535YU4jFw.mp4 21.3 MBPart 11-Module 01-Lesson 02_Naive Bayes/18. Gaussian NB Example-wpnDwiqTCJA.mp4 21.3 MBPart 06-Module 01-Lesson 01_Working with MongoDB/17. Range Queries-6r5Xc-_lhEI.mp4 21.2 MBPart 15-Module 01-Lesson 03_Creating Visualizations in Tableau/01. What is Tableau-LeCpU8HvVg8.mp4 21.1 MBPart 19-Module 01-Lesson 03_Data Visualizations in Tableau/01. What is Tableau-LeCpU8HvVg8.mp4 21.1 MBPart 18-Module 01-Lesson 01_Introduction to Data Wrangling/04. David's Data Wrangling Example-TN-CWy3GK44.mp4 21.0 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/10. Techniques to Gather Additional Data-6mTsvU0ML94.mp4 21.0 MBPart 14-Module 01-Lesson 04_Designing an Experiment/09. Unit of Analysis vs. Diversion Example-ycbXO4xBEVw.mp4 21.0 MBPart 11-Module 01-Lesson 05_Choose Your Own Algorithm/10. Welcome to the end of the lesson-nWFJ_eOU27I.mp4 20.9 MBPart 21-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.mp4 20.9 MBPart 12-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 20.9 MBPart 14-Module 01-Lesson 03_Choosing and Characterizing Metrics/23. Nonparametric Answers-Z0QcZHrflmc.mp4 20.7 MBPart 03-Module 01-Lesson 20_L8 Estimation/19. Engagement Ratio-EdIcXVIYX88.mp4 20.7 MBPart 03-Module 01-Lesson 20_L8 Estimation/10. 95 CI with Exact Z-Scores-apqUAtnsLZE.mp4 20.5 MBPart 05-Module 01-Lesson 03_Data in More Complex Formats/17. 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Up and Down the Visualization Spectrum-NdVf2bY8OnM.zh-CN.vtt 9.5 kBPart 03-Module 01-Lesson 04_L2 Visualizing Data/11. Number of Rows.html 9.5 kBPart 04-Module 01-Lesson 02_Jupyter notebooks/04. Notebook interface.html 9.5 kBPart 16-Module 01-Lesson 02_Jupyter notebooks/04. Notebook interface.html 9.5 kBPart 17-Module 01-Lesson 14_Regression/05. Quiz Linear Regression Language.html 9.5 kBPart 03-Module 01-Lesson 20_L8 Estimation/23. CI Bounds.html 9.5 kBPart 03-Module 01-Lesson 04_L2 Visualizing Data/07. Proportion from Countries.html 9.5 kBPart 21-Module 01-Lesson 02_Data Types and Operators/33. Solution Dictionaries and Identity Operators.html 9.5 kBPart 13-Module 01-Lesson 02_D3 Building Blocks/09. CSS Class Selectors.html 9.5 kBPart 03-Module 01-Lesson 20_L8 Estimation/21. Point Estimate Engagement Ratio.html 9.5 kBPart 13-Module 01-Lesson 02_D3 Building Blocks/14. Changing the Title.html 9.5 kBPart 20-Module 01-Lesson 01_Matrix Math and NumPy Refresher/07. Matrix Multiplication Part 2-8jtk8BzBdj8.pt-BR.vtt 9.5 kBPart 03-Module 01-Lesson 01_L1 Intro to Research Methods/18. Better Sample.html 9.5 kBPart 17-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Video Standard Deviation Calculation.html 9.5 kBPart 02-Module 01-Lesson 01_Intro to Research Methods/18. Better Sample.html 9.5 kBPart 17-Module 01-Lesson 14_Regression/08. Correlation Coefficients.html 9.5 kBPart 18-Module 03-Lesson 01_Assessing Data/03. Dataset Oral Insulin Phase II Clinical Trial Data.html 9.5 kBPart 21-Module 01-Lesson 02_Data Types and Operators/04. Solution Arithmetic Operators.html 9.5 kBPart 17-Module 01-Lesson 11_Confidence Intervals/15. Correct Interpretations of Confidence Intervals.html 9.5 kBPart 21-Module 01-Lesson 02_Data Types and Operators/12. Solution Booleans, Comparison and Logical Operators.html 9.5 kBPart 17-Module 01-Lesson 13_Case Study AB tests/15. 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DataFrame applymap()-Vp43h6ZRyoQ.zh-CN.vtt 1.1 kBPart 05-Module 01-Lesson 01_Data Extraction Fundamentals/13. Data Modeling in JSON--rnzkOofIv0.pt-BR.vtt 1.1 kBPart 21-Module 01-Lesson 07_Numpy Pandas - Part II/11. DataFrame applymap()-Vp43h6ZRyoQ.zh-CN.vtt 1.1 kBPart 03-Module 01-Lesson 23_L9 Hypothesis Testing/19. Null Hypothesis-Klv_8052hoA.en.vtt 1.1 kBPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/12. Affect t-Statistic-HMfd6rQrI0Y.zh-CN.vtt 1.1 kBPart 11-Module 01-Lesson 03_SVM/18. Practice Making a New Feature-ygveMIhCtDg.ja.vtt 1.1 kBPart 11-Module 01-Lesson 03_SVM/22. SVM C Parameter-WVg5-vxQDm8.ja.vtt 1.1 kBPart 18-Module 02-Lesson 01_Gathering Data/12. Source Downloading Files From The Internet II-M0qR7to1fl4.pt-BR.vtt 1.1 kBPart 18-Module 02-Lesson 01_Gathering Data/27. Conclusion-IanoSiET2nA.zh-CN.vtt 1.1 kBPart 21-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.zh-CN.vtt 1.1 kBPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/03. Degrees of Freedom-nJlJ0vlTRUs.en.vtt 1.1 kBPart 11-Module 01-Lesson 04_Decision Trees/07. Constructing A Decision TreeThird Split-1GCPKAYDPTg.pt-BR.vtt 1.1 kBPart 12-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.zh-CN.vtt 1.1 kBPart 13-Module 01-Lesson 04_Design Principles/18. Thinking Fast-CML5XbavX0w.pt-BR.vtt 1.1 kBPart 17-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.es-ES.vtt 1.1 kBPart 11-Module 01-Lesson 07_Regressions/21. Linear Regression Errors-A4nPMEOcUd4.zh-CN.vtt 1.1 kBPart 12-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.pt-BR.vtt 1.1 kBPart 13-Module 01-Lesson 04_Design Principles/30. Color Blind-mmw24xSEB3A.pt-BR.vtt 1.1 kBPart 17-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.ar.vtt 1.1 kBPart 18-Module 02-Lesson 01_Gathering Data/09. 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Meal Prices-gzu9UcNF0_k.zh-CN.vtt 1.1 kBPart 09-Module 01-Lesson 05_Explore Two Variables/15. Create Scatterplots-AtmfksGs-0I.ja.vtt 1.1 kBPart 09-Module 01-Lesson 07_Explore Many Variables/13. Friending Rate-ZO7y9tsSQ0A.zh-CN.vtt 1.1 kBPart 09-Module 01-Lesson 09_Diamonds Price Predictions/22. A Bigger, Better Data Set-uj6bLK91ZQI.ar.vtt 1.1 kBPart 11-Module 01-Lesson 04_Decision Trees/11. Min Samples Split-Mt5TWGYacJs.ja.vtt 1.1 kBPart 13-Module 01-Lesson 04_Design Principles/01. Lesson Overview-k5T30IHD8T8.pt-BR.vtt 1.1 kBPart 16-Module 02-Lesson 01_The Data Analysis Process/28. Conclusion-xYqxZQmXCdI.ar.vtt 1.1 kBPart 09-Module 01-Lesson 05_Explore Two Variables/21. A New Perspective-6BBUSWrSuFA.ja.vtt 1.1 kBPart 17-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.zh-Hans.vtt 1.1 kBPart 17-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.pt-BR.vtt 1.1 kBPart 03-Module 01-Lesson 13_L5 Standardizing/20. SD of Standardized Distribution-LuI1poHmFOw.zh-CN.vtt 1.1 kBPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/26. Rent - Cohen's d-admfaazbCnY.en.vtt 1.1 kBPart 11-Module 01-Lesson 02_Naive Bayes/13. From Scatterplots to Decision Surfaces-gbkORDbJM50.ar.vtt 1.1 kBPart 13-Module 01-Lesson 07_Narrative Structures/52. D3 Hides Complexity-iNXkYbkD3IU.pt-BR.vtt 1.1 kBPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/03. Degrees of Freedom-nJlJ0vlTRUs.pt-BR.vtt 1.1 kBPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/34. Keyboards - SD of Differences--wbCbKqqaZY.en.vtt 1.1 kBPart 03-Module 01-Lesson 28_L10b t-Tests, Part 2/06. r^2-X5F1reyQ_5U.zh-CN.vtt 1.1 kBPart 09-Module 01-Lesson 02_R Basics/01. The Power of R-Ca0MWoH_ZMY.en.vtt 1.1 kBPart 09-Module 01-Lesson 05_Explore Two Variables/18. More Caution with Correlation-sefN2vaxjSA.en-US.vtt 1.1 kBPart 13-Module 01-Lesson 04_Design Principles/01. Lesson Overview-k5T30IHD8T8.en.vtt 1.1 kBPart 13-Module 01-Lesson 04_Design Principles/30. 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NULLs-WYUkLKn6XCw.en.vtt 1.1 kBPart 03-Module 01-Lesson 20_L8 Estimation/07. Confidence Interval Bounds-Bs-wLbPOAfs.en.vtt 1.1 kBPart 11-Module 01-Lesson 02_Naive Bayes/03. Supervised Classification Example-buxApBhZCO0.zh-CN.vtt 1.1 kBPart 11-Module 01-Lesson 15_Evaluation Metrics/01. Welcome to Evaluation Metrics Lesson-IHuFWRM9f9Q.zh-CN.vtt 1.1 kBPart 13-Module 01-Lesson 01_Visualization Fundamentals/05. Slope Graphs-32WHsQi1UWc.pt-BR.vtt 1.1 kBPart 17-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.ja.vtt 1.1 kBPart 21-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.zh-CN.vtt 1.1 kBPart 04-Module 02-Lesson 03_NumPy and Pandas for 2D Data/12. DataFrame apply()-aGEUhSRys8w.en.vtt 1.1 kBPart 11-Module 01-Lesson 13_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.zh-CN.vtt 1.1 kBPart 21-Module 01-Lesson 07_Numpy Pandas - Part II/12. DataFrame apply()-aGEUhSRys8w.en.vtt 1.1 kBPart 03-Module 01-Lesson 03_PS 1b Additional Practice (Optional)/16. 34. Landmines-8ScnVyC24s8.en.vtt 1.1 kBPart 03-Module 01-Lesson 04_L2 Visualizing Data/11. Number of Rows-zxeKDuSK31M.zh-CN.vtt 1.1 kBPart 03-Module 01-Lesson 11_L4 Variability/38. Bessel's Correction-bHVPpomkUYs.en.vtt 1.1 kBPart 17-Module 01-Lesson 13_Case Study AB tests/09. Experiment II-fq4eO7CybA4.en.vtt 1.1 kBPart 03-Module 01-Lesson 18_L7 Sampling Distributions/37. Probability of Mean-RNgSdHPE4aA.pt-BR.vtt 1.1 kBPart 13-Module 01-Lesson 07_Narrative Structures/65. Lesson 3 Recap-MmJ6fBojjiA.en.vtt 1.1 kBPart 04-Module 02-Lesson 02_NumPy and Pandas for 1D Data/17. Plotting in Pandas-ujJShz9YfAU.zh-CN.vtt 1.1 kBPart 09-Module 01-Lesson 05_Explore Two Variables/18. More Caution with Correlation-TU63rBOwXQ8.zh-CN.vtt 1.1 kBPart 09-Module 01-Lesson 07_Explore Many Variables/25. Scatterplot Matrices-ov--BE6XTZU.ar.vtt 1.1 kBPart 11-Module 01-Lesson 04_Decision Trees/11. 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Create Scatterplots--gIxCjLXCEs.ja.vtt 1.1 kBPart 11-Module 01-Lesson 03_SVM/05. Practice with Margins-ICUYxC-8d7o.zh-CN.vtt 1.1 kBPart 18-Module 01-Lesson 01_Introduction to Data Wrangling/29. Conclusion-SRXNBlvW-xw.zh-CN.vtt 1.1 kBPart 21-Module 01-Lesson 07_Numpy Pandas - Part II/09. Pandas Axis Names-8vr8KQuJLcQ.zh-CN.vtt 1.1 kBPart 02-Module 01-Lesson 01_Intro to Research Methods/19. Randomness-KwmQDhUkLAE.en.vtt 1.1 kBPart 03-Module 01-Lesson 01_L1 Intro to Research Methods/19. Randomness-KwmQDhUkLAE.en.vtt 1.1 kBPart 03-Module 01-Lesson 23_L9 Hypothesis Testing/16. Fail to Reject the Null-7AGEhuipHl4.en.vtt 1.1 kBPart 09-Module 01-Lesson 09_Diamonds Price Predictions/24. Final Thoughts-CNGDocH1k3k.ja.vtt 1.1 kBPart 11-Module 01-Lesson 07_Regressions/18. AgeNet Worth Regression in sklearn-5aghWw9eIAM.en-US.vtt 1.1 kBPart 13-Module 01-Lesson 07_Narrative Structures/52. D3 Hides Complexity-iNXkYbkD3IU.en.vtt 1.1 kBPart 11-Module 01-Lesson 04_Decision Trees/26. 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Practice Finding Centers-FZVBF1HR4U0.ar.vtt 122 BytesPart 17-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.en.vtt 122 BytesPart 17-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.ja.vtt 122 BytesPart 17-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.ar.vtt 122 BytesPart 17-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.pt-BR.vtt 122 BytesPart 11-Module 01-Lesson 02_Naive Bayes/29. Total Probability-FlbDcNPGgUE.pt-BR.vtt 121 BytesPart 11-Module 01-Lesson 07_Regressions/03. Age Continuous or Discrete-kNxZwfXwvuk.ar.vtt 121 BytesPart 03-Module 01-Lesson 23_L9 Hypothesis Testing/37. Increase Engagement-o0gtllaVkuE.en.vtt 120 BytesPart 17-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.en.vtt 120 BytesPart 17-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.es-ES.vtt 120 BytesPart 03-Module 01-Lesson 18_L7 Sampling Distributions/05. Mean of Each Sample-SAdBKdiDE6w.en.vtt 119 BytesPart 03-Module 01-Lesson 20_L8 Estimation/32. What Does This Mean-Hqk1_dUlmo4.es-AR.vtt 119 BytesPart 03-Module 01-Lesson 20_L8 Estimation/32. What Does This Mean-Hqk1_dUlmo4.es-ES.vtt 119 BytesPart 11-Module 01-Lesson 04_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.ja.vtt 119 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-Hans.vtt 119 BytesPart 17-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.en.vtt 119 BytesPart 11-Module 01-Lesson 10_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt 118 BytesPart 17-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.en.vtt 118 BytesPart 17-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.zh-CN.vtt 118 BytesPart 17-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.ar.vtt 118 BytesPart 17-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.es-ES.vtt 118 BytesPart 17-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.ja.vtt 118 BytesPart 03-Module 01-Lesson 04_L2 Visualizing Data/19. Proportion over 60-u4rfZfA2ANk.zh-CN.vtt 117 BytesPart 17-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.zh-CN.vtt 117 BytesPart 03-Module 01-Lesson 04_L2 Visualizing Data/05. Range of Proportions--xSSuPcrrA8.zh-CN.vtt 116 BytesPart 03-Module 01-Lesson 11_L4 Variability/23. Average Absolute Deviation--S35dQNbjhc.zh-CN.vtt 116 BytesPart 03-Module 01-Lesson 20_L8 Estimation/32. What Does This Mean-Hqk1_dUlmo4.en-GB.vtt 116 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ja.vtt 116 BytesPart 17-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.zh-CN.vtt 116 BytesPart 03-Module 01-Lesson 20_L8 Estimation/32. What Does This Mean-Hqk1_dUlmo4.it.vtt 115 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-CN.vtt 115 BytesPart 03-Module 01-Lesson 20_L8 Estimation/32. What Does This Mean-Hqk1_dUlmo4.en.vtt 113 BytesPart 11-Module 01-Lesson 13_PCA/10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt 113 BytesPart 17-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.zh-CN.vtt 113 BytesPart 17-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.ja.vtt 113 BytesPart 17-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.zh-CN.vtt 113 BytesPart 03-Module 01-Lesson 18_L7 Sampling Distributions/05. Mean of Each Sample-SAdBKdiDE6w.pt-BR.vtt 111 BytesPart 11-Module 01-Lesson 04_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.pt-BR.vtt 111 BytesPart 17-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.zh-CN.vtt 111 BytesPart 11-Module 01-Lesson 02_Naive Bayes/28. Normalizing 3-L4elyDe8pFk.ar.vtt 110 BytesPart 11-Module 01-Lesson 02_Naive Bayes/28. Normalizing 3-L4elyDe8pFk.pt-BR.vtt 110 BytesPart 17-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.ar.vtt 110 BytesPart 17-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.ja.vtt 110 BytesPart 17-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.en.vtt 110 BytesPart 17-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.zh-CN.vtt 110 BytesPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/38. Keyboards - Cohen's d-9TgKqnlta2U.pt-BR.vtt 109 BytesPart 11-Module 01-Lesson 13_PCA/10. Practice Finding Centers-FZVBF1HR4U0.en.vtt 109 BytesPart 11-Module 01-Lesson 13_PCA/10. Practice Finding Centers-FZVBF1HR4U0.pt-BR.vtt 109 BytesPart 17-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.es-ES.vtt 109 BytesPart 17-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.es-ES.vtt 109 BytesPart 17-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.en.vtt 109 BytesPart 11-Module 01-Lesson 04_Decision Trees/28. Information Gain Calculation Part 7-frzL4n6Y-vU.en.vtt 108 BytesPart 11-Module 01-Lesson 10_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt 108 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ar.vtt 108 BytesPart 17-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.th.vtt 108 BytesPart 17-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.pt-BR.vtt 108 BytesPart 17-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.es-ES.vtt 108 BytesPart 17-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.pt-BR.vtt 108 BytesPart 17-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.zh-CN.vtt 108 BytesPart 11-Module 01-Lesson 02_Naive Bayes/28. Normalizing 3-L4elyDe8pFk.en.vtt 107 BytesPart 11-Module 01-Lesson 04_Decision Trees/26. Information Gain Calculation Part 5-4oOXVejgFGk.ja.vtt 107 BytesPart 11-Module 01-Lesson 10_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt 107 BytesPart 17-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.es-ES.vtt 107 BytesPart 03-Module 01-Lesson 08_L3 Central Tendency/19. Requirement for Median-T9tjPHqbB6M.pt-BR.vtt 106 BytesPart 11-Module 01-Lesson 02_Naive Bayes/27. Normalizing 2-rnaZpqIqA2g.ja.vtt 106 BytesPart 17-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.pt-BR.vtt 106 BytesPart 03-Module 01-Lesson 11_L4 Variability/31. Calculate SD-ZzEF7yc5208.en.vtt 105 BytesPart 11-Module 01-Lesson 10_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.pt-BR.vtt 105 BytesPart 03-Module 01-Lesson 08_L3 Central Tendency/19. Requirement for Median-T9tjPHqbB6M.zh-CN.vtt 104 BytesPart 03-Module 01-Lesson 18_L7 Sampling Distributions/11. Relationship Between SDs-nG9l9ba7TrE.en.vtt 104 BytesPart 11-Module 01-Lesson 02_Naive Bayes/28. Normalizing 3-L4elyDe8pFk.ja.vtt 104 BytesPart 11-Module 01-Lesson 10_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt 104 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.es-ES.vtt 104 BytesPart 17-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.en.vtt 104 BytesPart 03-Module 01-Lesson 11_L4 Variability/31. Calculate SD-ZzEF7yc5208.zh-CN.vtt 103 BytesPart 17-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.en.vtt 103 BytesPart 03-Module 01-Lesson 08_L3 Central Tendency/19. Requirement for Median-T9tjPHqbB6M.en.vtt 101 BytesPart 03-Module 01-Lesson 26_L10a t-Tests, Part 1/38. Keyboards - Cohen's d-9TgKqnlta2U.en.vtt 101 BytesPart 11-Module 01-Lesson 02_Naive Bayes/27. Normalizing 2-rnaZpqIqA2g.zh-CN.vtt 101 BytesPart 11-Module 01-Lesson 04_Decision Trees/26. Information Gain Calculation Part 5-4oOXVejgFGk.en.vtt 101 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.pt-BR.vtt 101 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.zh-Hans.vtt 101 BytesPart 17-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.es-ES.vtt 101 BytesPart 03-Module 01-Lesson 11_L4 Variability/31. Calculate SD-ZzEF7yc5208.pt-BR.vtt 100 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.ja.vtt 100 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.pt-BR.vtt 100 BytesPart 11-Module 01-Lesson 04_Decision Trees/16. Entropy Calculation Part 1-JX3NN5zwL08.ar.vtt 99 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.ar.vtt 99 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.pt-BR.vtt 99 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.zh-CN.vtt 99 BytesPart 17-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.pt-BR.vtt 99 BytesPart 03-Module 01-Lesson 08_L3 Central Tendency/19. 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Admissions 2-o91iPvtqt78.es-ES.vtt 95 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.en.vtt 95 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.hr.vtt 95 BytesPart 17-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.ar.vtt 95 BytesPart 17-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.en.vtt 95 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.zh-CN.vtt 94 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.ar.vtt 94 BytesPart 17-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.it.vtt 94 BytesPart 17-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.ja.vtt 94 BytesPart 17-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.pt-BR.vtt 94 BytesPart 11-Module 01-Lesson 02_Naive Bayes/27. Normalizing 2-rnaZpqIqA2g.ar.vtt 93 BytesPart 11-Module 01-Lesson 07_Regressions/03. 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