Udacity - AI for Trading v1.0.0 nd880 v1.0.0

磁力链接/BT种子名称

Udacity - AI for Trading v1.0.0 nd880 v1.0.0

磁力链接/BT种子简介

种子哈希：b2213011ecc9871cdc1f82c18a1a90f44b4d2e7c
文件大小： 7.32G
已经下载：4092次
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收录时间：2021-03-24
最近下载：2025-08-29

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文件列表

  • Part 01-Module 01-Lesson 06_Data Processing/14. Eddy Deven V2-g7zJV-Ontbo.mp4 62.8 MB
  • Part 02-Module 01-Lesson 01_Welcome To Term II/04. NewsRoom Liz V2-Os881EhJv68.mp4 58.9 MB
  • Part 02-Module 01-Lesson 01_Welcome To Term II/05. AITND Term II Interview W Justin V2 V2-JOkwa1brNX8.mp4 50.6 MB
  • Part 01-Module 01-Lesson 09_Project 1 Trading with Momentum/04. Eddy Thomas 02 AI And Finanace V2-8Hna_hR_N7c.mp4 38.8 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/03. M5 SC 15 10Ks Walkthrough V1-0ytyZ4LVG6s.mp4 38.7 MB
  • Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/05. Eddy Thomas 03 Jobs And Interviews V2-5W9XsgyxYHE.mp4 35.4 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/25. Kalman Filter Code Solution - Artificial Intelligence for Robotics-X7cixvcogl8.mp4 35.4 MB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.mp4 34.1 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/24. PyTorch - Part 8-S9F7MtJ5jls.mp4 30.9 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/16. NewsRoom Juan V2-DpO73QaXaOY.mp4 27.2 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/06. AIT M5L5 05 Frequency Reweighting V2-X-Cf2Uj7tH4.mp4 26.6 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.mp4 26.3 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/10. NewsRoom Liz Short V2-vqHsk3hjBLU.mp4 25.8 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/12. Eddy Thomas 01 Quants V2-ZRzhyaqz7I0.mp4 25.0 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.mp4 24.9 MB
  • Part 01-Module 04-Lesson 01_Factors/12. Zipline Pipeline SC V1-DHTwIwVk_sc.mp4 24.6 MB
  • Part 07-Module 01-Lesson 03_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 24.4 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/12. L4 13 Limitations V2-UbbZa7-3iuk.mp4 24.3 MB
  • Part 02-Module 05-Lesson 04_Project 8 Backtesting/03. AITND Finish V2-g-a4ZNIPQqw.mp4 24.2 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/10. M7L5 22 V1-34bV5mYUJAI.mp4 23.8 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/12. M5 SC 7 Metacharacters Part 2 V1-KK1xo8GDfvE.mp4 23.7 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.mp4 23.1 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 18 V1-7LESwsbt70s.mp4 23.0 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.mp4 23.0 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/02. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 22.7 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 22.7 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.mp4 22.7 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/03. M8L1 07 Backtest Validity V5-KTlrel9p6Q0.mp4 22.6 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/12. PyTorch V2 Part 3 Solution V2-zBWlOeX2sQM.mp4 22.3 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 22.1 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.mp4 22.0 MB
  • Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/09. Linear Transformations 1-99jYIxBRDww.mp4 21.8 MB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.mp4 21.6 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/02. AIT M5L4A 02 Financial Statement V6-XYff0ROHzWo.mp4 21.4 MB
  • Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/11. Linear Transformations 3-g_yTyRwMzXU.mp4 21.1 MB
  • Part 03-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.mp4 20.9 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/18. MV When You Dont Believe In Yourself 1 V1-rjCr-Z7UhZE.mp4 20.8 MB
  • Part 01-Module 04-Lesson 01_Factors/02. M4 L1A 02 Intro V2-W7_llXQ2GhA.mp4 20.7 MB
  • Part 02-Module 01-Lesson 03_Text Processing/06. Cleaning-qawXp9DPV6I.mp4 20.5 MB
  • Part 02-Module 05-Lesson 04_Project 8 Backtesting/01. NewsRoom Eddy 1 V2-9MlGvKWQn_o.mp4 20.2 MB
  • Part 03-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.mp4 19.9 MB
  • Part 01-Module 01-Lesson 06_Data Processing/11. M1L4 13 Exchange Traded Funds V4-Zx7v5GCfpvI.mp4 19.8 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/11. PyTorch V2 Part 3 V1-9ILiZwbi9dA.mp4 19.7 MB
  • Part 03-Module 01-Lesson 03_Control Flow/10. For Loops-UtX0PXSUCdY.mp4 19.3 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/20. 07 CharRNN Solution V1-ed33qePHrJM.mp4 19.2 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/17. MV 12 Embrace The Struggle V2-SGcgOm5kiKU.mp4 19.2 MB
  • Part 03-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.mp4 19.2 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.mp4 18.9 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/08. Jonathan Larkin Careers-QhHNPxM_Ku4.mp4 18.8 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/13. M5 SC 8 Metacharacters Part 3 V1-nDlxRlDUNHk.mp4 18.5 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/02. AIT M5L5 02 Readability V2-pcB63lIgiQg.mp4 18.5 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/14. M5 SC 9 Substitutions And Flags V1-9pxTGOlkLEY.mp4 18.4 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/25. PyTorch V2 Part 8 Solution V1-4n6T93hKRD4.mp4 18.3 MB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.mp4 18.2 MB
  • Part 03-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.mp4 18.2 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 6 V1-R8kfSvnHA3k.mp4 18.2 MB
  • Part 02-Module 01-Lesson 01_Welcome To Term II/01. AITND TII 01 Recap Of Term 1 V1-uhIvBfhcyLM.mp4 18.1 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/13. Strings-ySZDrs-nNqg.mp4 18.1 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/07. M4 L1B 06 Factor Models In Quant Finance V2-VeM2SudgZqc.mp4 18.1 MB
  • Part 03-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.mp4 17.8 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 12 V1-cHU7Sh12eOo.mp4 17.7 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.mp4 17.7 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.mp4 17.6 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/10. PyTorch V2 Part 2 Solution 2 V1-8KRX7HvqfP0.mp4 17.5 MB
  • Part 01-Module 02-Lesson 04_Time Series Modeling/04. M2L4 05 Advanced Time Series Models V5-cj1RCBTDog8.mp4 17.4 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.mp4 16.7 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/19. Saving Loading Models V1-psmrPu-mseA.mp4 16.7 MB
  • Part 03-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.mp4 16.7 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/17. PyTorch V2 Part 5 Solution V1-AjrXltxqsK4.mp4 16.6 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.mp4 16.6 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/17. 04 Implementing CharRNN V2-MMtgZXzFB10.mp4 16.5 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/07. L7 HS17 V1-k0vANVo-5Ek.mp4 16.5 MB
  • Part 03-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.mp4 16.5 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/05. PyTorch V2 Part 1 Solution V1-mNJ8CujTtpo.mp4 16.4 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/11. L4 12 Rebalancing Strategies V2-8u5gBx-fYr8.mp4 16.4 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/26. M4 L1B 25 Other Alternative Data V1-hMw3AuPVSSs.mp4 16.2 MB
  • Part 03-Module 01-Lesson 04_Functions/02. Defining Functions-IP_tJYhynbc.mp4 16.2 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/08. Números inteiros e floats-MiJ1vfWp-Ts.mp4 16.2 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.mp4 16.1 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/08. M4 L1B 07 Risk Factors V Alpha Factors V2-9KUpH1MDC1k.mp4 16.0 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/14. PCA Toy Problem SC V1-uyl44T12yU8.mp4 15.9 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/02. M2L2 02 Sources Of Outliers V8-gXKhKQ2_TaA.mp4 15.9 MB
  • Part 01-Module 02-Lesson 05_Volatility/12. M2L5 12 Using Volatility For Equity Trading V5-Vh9ajVRedvY.mp4 15.9 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/18. M5 SC 16 HTML Structure V1-R3QLtHxedXw.mp4 15.8 MB
  • Part 03-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.mp4 15.8 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/25. M4 L1B 24 NLP Used To Enhance Fundamental Analysis V1-9zMWuZ9j7rI.mp4 15.8 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.mp4 15.7 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/53. M4 L3a 27 Interlude Pt 3 V2-v6cLkoJhujU.mp4 15.5 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/12. 02 Time Series Prediction V2-xV5jHLFfJbQ.mp4 15.5 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/02. M8L1 06 What Is A Backtest V4-q2dW6-ZRaXE.mp4 15.5 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/52. M4 L3a 26 Interlude Pt 2 V2-1a60RPqhO8k.mp4 15.4 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/06. 4 Data Subsampling V1-7SJXv2BQzZA.mp4 15.4 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/09. M5 SC 4 Searching For Simple Patte V1-7RAHoJ34gXI.mp4 15.4 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/20. PyTorch - Part 7-hFu7GTfRWks.mp4 15.3 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 15.1 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/16. 12 CompleteModel CustomLoss V2-7SqNN_eUAdc.mp4 15.0 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/03. M4 L1B 03 Factor Returns As Latent Variables V3-LpHvJq6XTOQ.mp4 14.9 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/02. Jonathan Larkin - What Is A Quant-G22oM0qv0Hs.mp4 14.9 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/10. L4 11 Rebalancing A Portfolio V2-S5SPhBpG3b0.mp4 14.9 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 9 V1-x7ON_-QrpHQ.mp4 14.9 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/02. M4 L1B 02 What Is A Factor Model V4-K5QKPwU38Do.mp4 14.9 MB
  • Part 05-Module 01-Lesson 02_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.mp4 14.9 MB
  • Part 04-Module 01-Lesson 03_Linear Combination/02. Linear Combinations 2-RsKJNDTb8nw.mp4 14.9 MB
  • Part 01-Module 01-Lesson 06_Data Processing/05. M1L4 08 Missing Values V5-XaMaVFUIc_I.mp4 14.9 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/10. AffdexMe Demo-dpFtXDqakvY.mp4 14.6 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/06. Vision-based Emotion AI-7nKKWWn1sAc.mp4 14.4 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/11. 9 Model Validation Loss V2-GKDCq8J76tM.mp4 14.3 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/04. M8L1 08 Backtest Overfitting V2-MnWHGIiqjns.mp4 14.3 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/10. M5 SC 5 Word Boundaries V1-3dWIHULqKog.mp4 14.3 MB
  • Part 01-Module 04-Lesson 01_Factors/01. M4 L3A 01 Intro To The Factors V2-OqhRUxHf6wo.mp4 14.2 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/05. M4 L3a 04 Researching Alphas From Academic Papers V4-te0UTxemLBE.mp4 14.2 MB
  • Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/10. Linear Transformations 2-imtEd8M6__s.mp4 14.1 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/25. L2 06 Lists Methods V1-tz2Ja1Eaeqo.mp4 14.0 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.mp4 13.9 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.mp4 13.9 MB
  • Part 03-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.mp4 13.9 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.8 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/17. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.8 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.8 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/23. M5 SC 14 Searching The Parse Tree Part 3 V1-PR--1dLqcTM.mp4 13.7 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/50. M4 L3a 23 Summary V3-FZYqdaqoiZk.mp4 13.6 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/34. M4 L3a 151 The Fundamental Law Of Active Management Part 1 V4-iCW_vqvrTlw.mp4 13.6 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.mp4 13.5 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/16. PyTorch V2 Part 5 V1 (1)-XACXlkIdS7Y.mp4 13.4 MB
  • Part 03-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.mp4 13.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 51 Shap One Solution V2 (1)-Q0qAsUz2gnU.mp4 13.4 MB
  • Part 07-Module 01-Lesson 03_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 13.3 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/16. AIT M5L4B 06 Introduction To Beautifulsoup V3-k8e-kB3qBng.mp4 13.3 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/16. M4 L1B 15 Volume Factors V1-1dTAV3Irxv4.mp4 13.2 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/09. Entropy-piLpj1V1HEk.mp4 13.2 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 13.2 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.mp4 13.2 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.mp4 13.1 MB
  • Part 01-Module 02-Lesson 05_Volatility/11. M2L5 11 Markets Volatility V3-jEHJkZUX9s4.mp4 13.1 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/11. M7L5 28 V1-Ffax3lTKAs0.mp4 13.1 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/21. 08 Making Predictions V3-BhrpV3kwATo.mp4 13.0 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/11. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.9 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.9 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/04. PyTorch V2 Part 1 V1-6Z7WntXays8.mp4 12.9 MB
  • Part 01-Module 03-Lesson 02_ETFs/09. L2 11 2 Arbitrage Farmers Market V1-hHxp16mQNGA.mp4 12.8 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/22. M4 L1B 21 Analyst Ratings V1-cHkJo8qBKes.mp4 12.8 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.mp4 12.5 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.mp4 12.5 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/21. M5 SC 12 Searching The Parse Tree Part 1 V1-RyJuvYTF3Ms.mp4 12.5 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/07. M5 SC 2 Finding Words V1-wTOh9B6aHGk.mp4 12.3 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/24. M4 L3a 12 Return Denominator Leverage And Factor Returns V3-QxHrP5LoXAI.mp4 12.3 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/14. M4 L4 17 Path Dependency 1 V3-ok9rKYRtZLE.mp4 12.2 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.mp4 12.1 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/15. M4 L1B 14 PriceVolume Factors V2-zaG0PDc3wsA.mp4 12.1 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.mp4 12.0 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/07. M8L1 14 Structural Changes V1-EaxepBSycbQ.mp4 12.0 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.mp4 12.0 MB
  • Part 03-Module 01-Lesson 05_Scripting/20. Importing Files-qjeSn6zZbR0.mp4 12.0 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/10. 8 Word2vec Model V2-7BEYWhym8lI.mp4 11.9 MB
  • Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.mp4 11.9 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/30. M4 L3A 141 Ranked Information Coefficient Part 1 V4-_huNulOIuB0.mp4 11.9 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/05. AIT M5L4B 01 Introduction To Regex V4-WCXDD_n1ZuA.mp4 11.8 MB
  • Part 01-Module 02-Lesson 03_Regression/06. Testing For Normalilty-Sa1MJegyYfc.mp4 11.8 MB
  • Part 06-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.mp4 11.8 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/11. L3 09 Capital Market Line V2-BRO-vo3y0-U.mp4 11.8 MB
  • Part 06-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.mp4 11.8 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/04. 04. Computer Vision Applications-aFJKp2NltCY.mp4 11.7 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.mp4 11.7 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/12. M4 L3b 09 Winners And Losers Creating A Joint Factor V3-xmW05ii8Vxs.mp4 11.7 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/22. M4 L3b 18 IVol Value Fundamental Or Discretionary Investing V2-sKAE5Z8e7IM.mp4 11.7 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/07. AIT M5L5 08 Similarity Analysis V3-LNro_9JOIrY.mp4 11.7 MB
  • Part 01-Module 02-Lesson 04_Time Series Modeling/06. M2L4 07 Kalman Filter V4-CLJhgfMI4Ho.mp4 11.6 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.mp4 11.6 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.mp4 11.6 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/20. M5 SC 11 Navigating The Parse Tree V1-NzOB9Vyy0l4.mp4 11.5 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.mp4 11.5 MB
  • Part 03-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.mp4 11.4 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/05. 05. Emotional Intelligence-D_LzJsJH5qk.mp4 11.4 MB
  • Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.mp4 11.3 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.mp4 11.3 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/01. M4 L2A 01 Intro V1-DgsD3yL9Yy0.mp4 11.3 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.mp4 11.3 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/14. M4 L3b 10 Skewness And Momentum Attentional Bias V3-3ZkFRBUmSQ0.mp4 11.3 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.mp4 11.2 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/10. M4 L4 10 Estimation Error V4-WdrMIRhScN0.mp4 11.2 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/02. Introduction-XZL934YQ-FQ.mp4 11.2 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.mp4 11.2 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/05. M4 L3b 04 Overnight Returns Data Universe Methods V2-Y_lBDa1hRco.mp4 11.1 MB
  • Part 03-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.mp4 11.1 MB
  • Part 07-Module 01-Lesson 03_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 11.0 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/04. M2L2 03 Outliers Signals And Strategies V5-zyVgpsRy-mU.mp4 11.0 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/10. M4 L1B 09 Risk Factors V Alpha Factors Part 3 V1-UmdOVhcRCVU.mp4 11.0 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/21. M4 L1B 20 Pre And Post Event V1-Olz9QZQaBxs.mp4 11.0 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/03. M8l2 02 Barra Data Take2 V1-7WjYfvpLSTM.mp4 11.0 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/03. L4 03 Optimization With Constraints V3-91WzhG6dti8.mp4 10.9 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/04. AIT M5L5 04 BagofWords V3-8t2bf9kAVHE.mp4 10.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 50 Shap One Intro V1 (1)-BKD_FseE6Z4.mp4 10.8 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/11. M4 L1B 10 Risk Factors V Alpha Factors Part 4 V1-3ZE298YwbCM.mp4 10.8 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.mp4 10.7 MB
  • Part 01-Module 01-Lesson 09_Project 1 Trading with Momentum/01. MV 03 Transition To Project 01 V1-dcps5Bg4bZE.mp4 10.7 MB
  • Part 01-Module 02-Lesson 03_Regression/01. M2L3 01 Intro V4-C7vWJH05tKA.mp4 10.7 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.mp4 10.6 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/01. M4 L3a 01 Intro Efficient Market Hypothesis And Arbitrage Opportunities V3--YpXAt7zuh8.mp4 10.6 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/04. 3 Data PreProcessing V1-Xw1MWmql7no.mp4 10.6 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/46. M4 L3a 20 Transfer Coefficient V3-4rZ0MWQzlIs.mp4 10.6 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/08. PyTorch V2 Part 2 V1-CSQOdOb2mlg.mp4 10.5 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/09. M4 L3a 06 Ranking Part 1 V4-4j2hIB7WHY4.mp4 10.5 MB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.mp4 10.5 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 53 Additive Feature Att Part 2 V1-ah171-grqus.mp4 10.4 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.mp4 10.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/03. M1L1 Introducing The Instructors 1 V4-l5gG7r-BWYc.mp4 10.3 MB
  • Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/01. MV 7 Transition To Project 02 1 V1-nkAcx2X_lfs.mp4 10.3 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/39. M4 L3a 172 Factor Rank Autocorrelation Turnover V2-QBvbMiVW100.mp4 10.3 MB
  • Part 05-Module 01-Lesson 02_NumPy/08. NumPy 4 V1-jeU7lLgyMms.mp4 10.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 43 Prediction Solution Part 1 V2-VgtWMV2GIic.mp4 10.3 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/03. M2L6 04 Pairs Trading V3-7lEm_tFXcBk.mp4 10.2 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Solution Part 2 V1-UhtIVQ8YGvI.mp4 10.2 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/04. M1L1 05 Program Overview V1-Ci0j_UwLlQQ.mp4 10.2 MB
  • Part 02-Module 03-Lesson 04_Random Forests/01. L4 01 HS Intro V2-9c5d6MvguA0.mp4 10.2 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.mp4 10.1 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/13. 03 Training Memory V1-sx7T_KP5v9I.mp4 10.0 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/01. MV 11 Intro To Module 03 Difficulties In Learning V1-kqjFkUVZwEc.mp4 10.0 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/12. 10 NegativeSampling V1-gnCwdegYNsQ.mp4 10.0 MB
  • Part 01-Module 03-Lesson 02_ETFs/12. MV 11 Guided Meditation V1-njp1mnEEv9s.mp4 9.9 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/13. M4 L1B 12 How An Alpha Factor Becomes A Risk Factor Part 2 V1-9waaTtRaU-Y.mp4 9.9 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.mp4 9.9 MB
  • Part 02-Module 01-Lesson 07_Project 5 NLP on Financial Statements/01. Intro Term II V2-jSK9Pr7wFQo.mp4 9.8 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.8 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/51. M4 L3a 25 Interlude Pt 1 V2-SMQwc5kwSr0.mp4 9.7 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.mp4 9.7 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.mp4 9.7 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/16. Information Gain-k9iZL53PAmw.mp4 9.7 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4 9.7 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/15. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.7 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.6 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/49. M4 L3a 22 Conditional Factors V2-2J1aUwGq6tc.mp4 9.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 24 CaseB Solution T2 V1-lNlhJhtCdxE.mp4 9.5 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/19. 06 Defining Model V2-_LWzyqq4hCY.mp4 9.5 MB
  • Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.mp4 9.5 MB
  • Part 03-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.mp4 9.5 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/09. 8 TensorDataset Batching V1-Oxuf2QIPjj4.mp4 9.5 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/05. M4 L1B 04 Factor Model Assumptions V3-qEu3m_3eGWk.mp4 9.4 MB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 9.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 42 Prediction Intro Part 2 V1-DG-HiRum1JU.mp4 9.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 22 CaseA Part 2 V1-Kuwa4DkNBZs.mp4 9.3 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/24. M4 L1B 23 Sentiment Analysis On News And Social Media V1-Jph7h2Yl0yg.mp4 9.3 MB
  • Part 03-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.mp4 9.3 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/09. PyTorch V2 Part 2 Solution V1-zym36ihtOMY.mp4 9.2 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 9.2 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/13. M4 L2b 14 Explained Variance V3-OdHeReNUqoQ.mp4 9.1 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/11. M5 SC 6 Metacharacters Part 1 V1-Jay3euM62NQ.mp4 9.0 MB
  • Part 04-Module 01-Lesson 01_Introduction/03. Essence Of Linear Algebra Intro -EHcxDZpeGFg.mp4 9.0 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.mp4 9.0 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/16. M4 L3b 12 Skewness And Momentum Momentum Enhances Or Weakened By Skew V2-S73J_h8DHsE.mp4 9.0 MB
  • Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/04. MV 10 Transition From Project 02 Int V1-DYjOsL3VYfY.mp4 8.9 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/07. M2L2 06 Spotting Outliers In Signal Returns V4-BSLGZz0RzTg.mp4 8.9 MB
  • Part 05-Module 01-Lesson 02_NumPy/07. NumPy 3 V1-Rt4aydeo9F8.mp4 8.9 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.9 MB
  • Part 03-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.mp4 8.8 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian - Artificial Intelligence for Robotics-fRYtUP0P4Lg.mp4 8.7 MB
  • Part 04-Module 01-Lesson 03_Linear Combination/01. Linear Combinations 1-fmal7UE7dEE.mp4 8.7 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.mp4 8.7 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/15. PyTorch V2 Part 4 Solution V1-R6Y4hPLVQWM.mp4 8.6 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/18. M4 L1B 17 Fundamental Ratios V2-Eo-faV9CsP8.mp4 8.6 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/15. M4 L4 19 What Is Optimization Doing To OUr Alphas V3-6Yqb91Xahvg.mp4 8.6 MB
  • Part 01-Module 02-Lesson 01_Quant Workflow/05. M2L1 04 Anatomy Of A Strategy Part 3 V1-vSxnkduTWWY.mp4 8.6 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.mp4 8.6 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 1 V2-IXlHYPjcQ-o.mp4 8.6 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/05. M8L1 10 Overtrading V2-0cdGLRDI_Sk.mp4 8.6 MB
  • Part 07-Module 01-Lesson 03_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4 8.5 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/03. M4 L4 03 Setting Up The Problem Risk V4-2vcULOlXTzc.mp4 8.5 MB
  • Part 01-Module 02-Lesson 05_Volatility/08. M2L5 07 Exponentially Weighted Moving Average V4-VBPitTHzYRI.mp4 8.5 MB
  • Part 05-Module 01-Lesson 03_Pandas/12. Pandas 7 V1-ruTYp-twXO0.mp4 8.5 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/31. M4 L3A 142 Ranked Information Coefficient Part 2 V5-WKGmog0Nzgo.mp4 8.5 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 4 V1-l614btyXRCI.mp4 8.4 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/09. 09. Training a Model-m4GVfwVkj74.mp4 8.4 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/12. Entropy Formula-w73JTBVeyjE.mp4 8.4 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.4 MB
  • Part 03-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.mp4 8.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 1 V3-1lkQSp6FHgo.mp4 8.3 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/06. M2L2 05 Handling Outliers In Raw Data V3-3l6kQZqlVJA.mp4 8.3 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.mp4 8.3 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/06. M4 L4 07 Leverage Constraint V5-zJ9gon4rFQc.mp4 8.3 MB
  • Part 05-Module 01-Lesson 03_Pandas/10. Pandas 6 V1-GS1kj04XQcM.mp4 8.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 2 V1-ZxfpUIY_AcE.mp4 8.2 MB
  • Part 05-Module 01-Lesson 03_Pandas/09. Pandas 5 V1-lClsJnZn_7w.mp4 8.2 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/04. M2L6 07 Finding Pairs To Trade V4-6hQtoElcnGM.mp4 8.2 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/04. M4 L3a 03 Definition Of Key Words V4-zySdIQTPTGo.mp4 8.2 MB
  • Part 01-Module 02-Lesson 03_Regression/11. M2L3 10 Linear Regression V4-GRY4eakMBJ8.mp4 8.2 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/01. Welcome to Computer Vision-GgA3_-MMT_I.mp4 8.1 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/35. M4 L3a 152 The Fundamental Law Of Active Management Part 2 V7-CMc4ujA8Ahs.mp4 8.1 MB
  • Part 01-Module 02-Lesson 04_Time Series Modeling/08. M2L4 09 Recurrent Neural Networks V5-5cYAAHyRHDo.mp4 8.1 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.mp4 8.1 MB
  • Part 01-Module 02-Lesson 05_Volatility/01. M2L5 01 What Is Volatility V3-brGVwpDSuG4.mp4 8.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 10 Node Importance Intro V1-Q7KJgKCm8cs.mp4 8.1 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/06. M4 L3a 051 Controlling For Risk Within An Alpha Factor Part 1 V3-raeVfAbBXnA.mp4 8.1 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/09. M4 L1B 08 Risk Factors V Alpha Factors Part 2 V2-AApfsuSpnMY.mp4 8.1 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.mp4 8.0 MB
  • Part 03-Module 01-Lesson 03_Control Flow/02. Indentation-G8qUNOTHtrM.mp4 8.0 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/22. M5 SC 13 Searching The Parse Tree Part 2 V1-WS_bkGCk7qk.mp4 8.0 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.mp4 8.0 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 13 Marketvol Solution V1-yR1eWiv1jTM.mp4 8.0 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/36. M4 L3a 161 Real World Constraints Liquidity V3-eu0YZRMu_3w.mp4 8.0 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/02. M1L3 02 Farmers Market V3-i_itXOdetCc.mp4 7.9 MB
  • Part 01-Module 03-Lesson 02_ETFs/06. L2 08 Authorized Participant And The Create Process V4-u4thSf3Uxsc.mp4 7.9 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/07. Regularization-ndYnUrx8xvs.mp4 7.9 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/11. M4 L4 12 Infeasible Problems V4-ljg25Rj511Q.mp4 7.9 MB
  • Part 05-Module 01-Lesson 02_NumPy/04. NumPy 1 V1-EOHW29kDg7w.mp4 7.9 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.mp4 7.9 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.mp4 7.8 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.mp4 7.8 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/13. M4 L3a 08 Z Score V3-6_cKCoLa92o.mp4 7.8 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/24. M4 L3b 20 IVol Volatility Enhanced Price Earnings Ratio V2-x-1nqTEPGcA.mp4 7.8 MB
  • Part 03-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.mp4 7.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 20 Shap Intro V1-AhZN3Bv_OCg.mp4 7.8 MB
  • Part 01-Module 04-Lesson 01_Factors/03. M4 L1A 03 Example Of A Factor V4-MJrwJDjWlAg.mp4 7.8 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/01. M2L6 01 Intro V3-CQ6QGAxbUF8.mp4 7.8 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 15 V1 (1)-Q5CZxxKXAB8.mp4 7.8 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.mp4 7.7 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 53 Additive Feature Att Part 1 V2-uTpfEfHp_KA.mp4 7.7 MB
  • Part 03-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.mp4 7.7 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.mp4 7.7 MB
  • Part 01-Module 02-Lesson 01_Quant Workflow/02. M2L1 01 Starting From A Hypothesis V3-yjlt4yerB9I.mp4 7.7 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/06. M4 L2b 06 The Core Idea V3-0KwLkaKHAvg.mp4 7.7 MB
  • Part 07-Module 01-Lesson 03_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.7 MB
  • Part 01-Module 02-Lesson 05_Volatility/02. M2L5 02 Historical Volatility V3-BOPhsYLHkUU.mp4 7.7 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/08. M2L2 07 Handling Outliers In Signal Returns V4-ILdnNi4CgZM.mp4 7.7 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.mp4 7.6 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/19. What Is A P-value Anyway-eU6pUZjqviA.mp4 7.6 MB
  • Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.6 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.6 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.mp4 7.6 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/19. M4 L3a 10 Factor Returns V5-enyeTpyCS-o.mp4 7.6 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/10. L3 08 The Efficient Frontier V3-tEEyhU23bI4.mp4 7.6 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/07. M4 L3a 052 Controlling For Risk Within An Alpha Factor Part 2 V2-Ks8HiHcflPs.mp4 7.6 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 21 Optional Explanation Part 1 V2-7shDUICrpro.mp4 7.5 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.mp4 7.5 MB
  • Part 05-Module 01-Lesson 01_Jupyter Notebooks/02. Jupyter-qiYDWFLyXvg.mp4 7.5 MB
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  • Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.mp4 7.4 MB
  • Part 01-Module 01-Lesson 07_Stock Returns/05. M1L5 06 Distribution Of Stock Prices Part 2 V1-cGoXGiO1DYk.mp4 7.4 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/03. M1L3 03 Trading Stocks V3-GHoRtfUrUMc.mp4 7.4 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/21. M4 L3b 17 IVol Idiosyncratic Volatility V2-B8hOR4G9CJk.mp4 7.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 3 V1-e6SZrnZxaTI.mp4 7.4 MB
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  • Part 06-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.mp4 7.3 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/01. M4 L2b 01 PCA Statistical Risk Model V1-lDxqJ0JYUzs.mp4 7.3 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/01. L3 01 Intro V1-PxLJniuGyC0.mp4 7.3 MB
  • Part 05-Module 01-Lesson 03_Pandas/08. Pandas 4 V1-eMHUn9v9dds.mp4 7.3 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/06. M4 L1B 05 Covariance Matrix Using Factor Model V3-_qfTLXoifsM.mp4 7.3 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 7.3 MB
  • Part 07-Module 01-Lesson 03_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 7.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 22 CaseA Part 1 V2-2X9531oXeJY.mp4 7.2 MB
  • Part 02-Module 01-Lesson 01_Welcome To Term II/02. AITND TII 02 Overview Of Term 2 V1-dVz-lVGvadY.mp4 7.2 MB
  • Part 01-Module 01-Lesson 06_Data Processing/04. M1L4 06 Technical Indicators V6-jo740Kq3YN4.mp4 7.2 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/06. M5 SC 1 Raw Strings V1-WhL1VbulThY.mp4 7.2 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/21. PyTorch V2 Part 7 Solution V1-d_NhvI1yEf0.mp4 7.1 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.mp4 7.1 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/02. 02. What Is Vision-_99V1rUNFa4.mp4 7.1 MB
  • Part 01-Module 02-Lesson 05_Volatility/03. M2L5 03 Annualized Volatility V8-yakh1pjP7uY.mp4 7.1 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.mp4 7.0 MB
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  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/02. M4 L4 02 Setting Up The Problem Alphas V5-6GeyU-thC4U.mp4 7.0 MB
  • Part 03-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.mp4 7.0 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.mp4 7.0 MB
  • Part 01-Module 01-Lesson 04_Stock Prices/02. M1L2 02 Stock Prices V7-l_PilXVuh8I.mp4 7.0 MB
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  • Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.mp4 6.9 MB
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.9 MB
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  • Part 02-Module 03-Lesson 05_Feature Engineering/03. M7L5 03 Setup Code Exercise V1-xlwlc6l7pXg.mp4 6.9 MB
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/25. L1 29 Open End Funds Holding Cash For Withdrawals V3-RU8-ZRBJ2Cw.mp4 6.8 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/20. M4 L1B 19 Index Changes V1-C7QNfPZBXXo.mp4 6.8 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/05. 4 EncodingWords Sol V1-4RYyn3zv1Hg.mp4 6.8 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4 6.7 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/15. M4 L3b 11 Skewness And Momentum Defining Skew V2-6PgqIpmJBJ8.mp4 6.7 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/26. L1 30 ClosedEnd Mutual Funds V3-y2VhtrF6vdc.mp4 6.7 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/07. 6 Cleaning And Padding V1-UgPo1_cq-0g.mp4 6.7 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/feb-26-2019-16-19-56.gif 6.6 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.mp4 6.6 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/05. Recommending Apps-nEvW8B1HNq4.mp4 6.6 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.6 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/09. M2L6 13 Trade Pairs Of Stocks V6-i1yVMrgjtB0.mp4 6.6 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.mp4 6.6 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/17. New Mean and Variance Solution - Artificial Intelligence for Robotics-SwxRWZaC1FM.mp4 6.6 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.mp4 6.6 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/02. L1 01 Stocks V2-XHo5iyxDxOQ.mp4 6.6 MB
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  • Part 06-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.mp4 6.6 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/09. M1L6 09 Statistical Analysis V10-_p1m_q8jE6E.mp4 6.6 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.mp4 6.6 MB
  • Part 01-Module 02-Lesson 03_Regression/04. M2L3 04 Parameters Of A Distribution V3--akdmiLDny4.mp4 6.6 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/01. M7L5 Intro V1-lSJ3XajG1L8.mp4 6.5 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/05. M4 L4 06 Standard Constraints V4-OPBKsNQPr6I.mp4 6.5 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/03. M4 L3a 02 Alpha Factors Versus Risk Factor Modeling V2-qsahBvhVTkk.mp4 6.5 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.5 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.5 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 62 Shap V1-KidZpvQ9Sus.mp4 6.4 MB
  • Part 01-Module 02-Lesson 03_Regression/02. M2L3 02 Distributions V2-ZlRGxq5I9BU.mp4 6.4 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.4 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.mp4 6.4 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/09. 7 Batching Data Solution V1-nu2rjLzt1HI.mp4 6.4 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.mp4 6.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/11. MV 05 Time Management V1-22PdQNlhCt8.mp4 6.3 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/15. M4 L2A 10 Portfolio Variance Using Factor Model V4-V06aCZUvgbo.mp4 6.3 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/19. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.3 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/01. AIT M5L5 01 Introduction V2-94CP-oy5KKI.mp4 6.3 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/02. M4L52 HSA Embedding Weight Matrix V3 RENDER V2-KVCcG5v8fi0.mp4 6.3 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/01. AIT M5L4A 01 Intro V1-BS4n9rRYGtw.mp4 6.3 MB
  • Part 01-Module 01-Lesson 07_Stock Returns/03. M1L5 03 Log Returns V5-62fZN1QnGjc.mp4 6.2 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 26 CaseC Solution V1-jcAiG0nLByI.mp4 6.2 MB
  • Part 01-Module 04-Lesson 01_Factors/05. M4 L1A 04 Standardizing A Factor V5-sLZY2SQ4uME.mp4 6.2 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.mp4 6.2 MB
  • Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Part II-1GRv1S6K8gQ.mp4 6.2 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/07. PyTorch V2 Part 1 Solution 3 V1-iMIo9p5iSbE.mp4 6.2 MB
  • Part 03-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.mp4 6.2 MB
  • Part 01-Module 02-Lesson 03_Regression/15. M2L3 14 Regression In Trading V2-bcOGRWxg7qQ.mp4 6.1 MB
  • Part 02-Module 03-Lesson 04_Random Forests/08. L4 15 HS Outofbag Score V4-CcdXrGYaOhE.mp4 6.1 MB
  • Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.mp4 6.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 23 CaseB Intro V1-8RKZrweh0Mw.mp4 6.1 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/10. M4 L3b 08 Winners And Losers Approximating Curves With Polynomials V4-Nw6v2EeECt0.mp4 6.1 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/18. 05 Batching Data V1-9Eg0wf3eW-k.mp4 6.1 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/27. L1 31 Transaction Costs V2-JGYAv7tQpyY.mp4 6.1 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 4 V1-Eg9DvNMi1v8.mp4 6.1 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/12. L1 13 Hang Seng Index Construction V2-rdGdC-meRLU.mp4 6.0 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/02. M1L6 02 Momentumbased Signals V4-RedwbmYg6e4.mp4 6.0 MB
  • Part 01-Module 04-Lesson 01_Factors/09. M4 L1A 08 Rescale Part 2 V3-8Ix10U6MEug.mp4 6.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.mp4 6.0 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/11. M7L5 Outro V1-k7DsfRhDwLQ.mp4 6.0 MB
  • Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.mp4 6.0 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/17. M4 L1B 16 Fundamentals V1-rPii5-ry8nc.mp4 6.0 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/23. M4 L3b 19 IVol Quantamental Investing V2-K6Ud6gams-U.mp4 6.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/27. MV When Those Around You Dont Believe In You V1--vKspTOIXY0.mp4 6.0 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/22. L2 09 Lists And Membership Operators V2-rNV_E50wcWM.mp4 5.9 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/08. 6 Defining Context Targets V1-DJN9MzD7ctY.mp4 5.9 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/02. L3 02 Diversification V3-tyzqlXddXd8.mp4 5.9 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/20. Notation for Random Variables-8NxTW1u4s-Y.mp4 5.9 MB
  • Part 03-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.mp4 5.9 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/03. Testing-gmxGRJSKEb0.mp4 5.9 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/10. 9 DefiningModel V1-SpvIZl1YQRI.mp4 5.9 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Part 2 V1-1WK5VAMzQ2Q.mp4 5.9 MB
  • Part 01-Module 03-Lesson 02_ETFs/02. L2 12 Shortcomings Of Mutual Funds V2-oEqsaex31Qg.mp4 5.9 MB
  • Part 01-Module 02-Lesson 05_Volatility/10. M2L5 09 Forecasting Volatility V3-82v4v_PKDAE.mp4 5.9 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/06. M4 L3b 05 Overnight Returns Methods Quantile Analysis V3-4Js3mghq2mU.mp4 5.9 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/24. L1 27 OpenEnd Mutual Funds V2-T4_mmjEKUAo.mp4 5.9 MB
  • Part 03-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.mp4 5.9 MB
  • Part 01-Module 02-Lesson 03_Regression/18. MV 14 What Happens In Your Brain V1-ioDP7ndd40Y.mp4 5.9 MB
  • Part 02-Module 01-Lesson 04_Feature Extraction/09. T-SNE-xxcK8oZ6_WE.mp4 5.8 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.mp4 5.8 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/01. M4 L3b 01 Case Studies Intro V3-oWWrWbzDi2k.mp4 5.8 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/03. M4 L3b 02 Overnight Returns Abstract V2-q5xidwa5W8w.mp4 5.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 41 Wrapper Solution V1-27tEa_Bpq20.mp4 5.8 MB
  • Part 01-Module 04-Lesson 01_Factors/06. M4 L1A 05 Demean Part 1 V3-R3N8bd8U6TM.mp4 5.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 49 Subsets Solution V1-YfwUDZ_hNKI.mp4 5.8 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/38. M4 L3a 171 Turnover As Proxy For Real World Constraints V2-6xo8sZjoSVk.mp4 5.8 MB
  • Part 06-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.mp4 5.8 MB
  • Part 01-Module 03-Lesson 02_ETFs/01. L2 01 Intro V2-utlPzT8MEsM.mp4 5.7 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/08. 7 PaddedFeatures Sol V1-sYOd1IDmep8.mp4 5.7 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/06. M2L6 09 Cointegration V6-N4ZI5SyFMOc.mp4 5.7 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/45. M4 L3a 19 Quantiles Academic Research Vs Practitioners V2-AwL7cV2VyhM.mp4 5.7 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.mp4 5.7 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/16. M4 L4 20 Outro V1-c3J8t6q2BGo.mp4 5.7 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/06. L1 07 Ratios V2-Dfbwep-tkok.mp4 5.7 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/08. M4 L2b 08 Writing It Down Pt 1 V3-NyDNFqm8c_s.mp4 5.7 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/07. M4 L4 08 Factor Exposure And Position Constraints V3-wMY4zI5zLSM.mp4 5.7 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/08. Student Admissions-TdgBi6LtOB8.mp4 5.7 MB
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  • Part 07-Module 01-Lesson 02_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.7 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/03. L1 02 Indices V2-BRv5B78YBGs.mp4 5.7 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/14. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.7 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/23. M4 L3a 11 Universe Construction Rule V3-Cr0-k7gUSNg.mp4 5.7 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/04. L3 04 Portfolio Variance V2-LlxRypakop4.mp4 5.7 MB
  • Part 01-Module 04-Lesson 09_Project 4 Alpha Research and Factor Modeling/01. M4 01 Intro To Project 4 V1-7goOG7CdUjU.mp4 5.7 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 22 CaseA Part 3 V1-EM21p54qrp0.mp4 5.7 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.mp4 5.6 MB
  • Part 01-Module 02-Lesson 01_Quant Workflow/04. M2L1 03 Flavors Of Trading Strategies V4-uCCx8I9u_Nk.mp4 5.6 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.mp4 5.6 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/02. Structured Languages-NsmqUIHlk6U.mp4 5.6 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/03. Structured Languages-NsmqUIHlk6U.mp4 5.6 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 16 Sector Intro V2-NSp_qX8s_B0.mp4 5.6 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
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  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.6 MB
  • Part 03-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules-jPGyFgcIvsM.mp4 5.6 MB
  • Part 02-Module 03-Lesson 06_Overlapping Labels/08. L6 11 HS Foreshadow V2-iXqYUwpFTqs.mp4 5.6 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.mp4 5.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/03. L7 H4 V1-CHo9WLLAOl0.mp4 5.6 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/04. M4 L2b 04 Bases As Languages V3-yEL0-AE3mjo.mp4 5.6 MB
  • Part 02-Module 03-Lesson 01_Overview/01. L1 01 HS Welcome To M7 V2-3l--nlBdfaA.mp4 5.5 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.mp4 5.5 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Solution V1-FBOlA2NaK3k.mp4 5.5 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 21 Optional Explanation Part 3 V1-xkhSC032Fxs.mp4 5.5 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/11. L1 12 How An Index Is Constructed V2-dsbi4dvdU9c.mp4 5.5 MB
  • Part 06-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.mp4 5.5 MB
  • Part 03-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.mp4 5.5 MB
  • Part 01-Module 01-Lesson 07_Stock Returns/01. M1L5 02 Returns V6-PngIo6G73Z8.mp4 5.5 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.mp4 5.5 MB
  • Part 03-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.mp4 5.5 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.mp4 5.5 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/01. M4L51 HSA Word Embeddings V3 RENDER V1-ZsLhh1mly9k.mp4 5.4 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/04. M4 L3b 03 Overnight Returns Possible Alpha Factors V2-QBCDr9q2rLo.mp4 5.4 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.4 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.mp4 5.4 MB
  • Part 01-Module 01-Lesson 06_Data Processing/03. M1L4 04 Corporate Actions V5-S60WArbQK7k.mp4 5.4 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.4 MB
  • Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.4 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.mp4 5.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 35 Tree Shap Intro V1-Si3r4-VR0CU.mp4 5.3 MB
  • Part 05-Module 01-Lesson 02_NumPy/09. NumPy 5 V1-vGjI-WTnEbY.mp4 5.3 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.mp4 5.3 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/14. L1 16 Funds V2-s9f2Bzc9lnk.mp4 5.3 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/04. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/06. M8l2 06 Scaling Alpha V2--gXKutS0jQc.mp4 5.3 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.mp4 5.3 MB
  • Part 03-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.mp4 5.2 MB
  • Part 01-Module 02-Lesson 01_Quant Workflow/05. M2L1 04 Anatomy Of A Strategy Part 2 V1-v3w4JZKQixc.mp4 5.2 MB
  • Part 01-Module 02-Lesson 01_Quant Workflow/01. MV 05 Intro To Module 2 V1-92JzOXda9Q8.mp4 5.2 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/01. Welcome to NLP-g-AlFF61p0I.mp4 5.2 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/02. Welcome to NLP-g-AlFF61p0I.mp4 5.2 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/12. M4 L4 14 Transaction Costs V3-yxwqTvbJhhc.mp4 5.2 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/02. L4 02 What Is Optimization V2-ISRlP1GeOjU.mp4 5.2 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/15. M82 Outro V1-GbWg0wSTDfk.mp4 5.2 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/15. L1 18 Alpha And Beta V3-CcVdfrr5nD8.mp4 5.2 MB
  • Part 02-Module 01-Lesson 03_Text Processing/10. Stemming And Lemmatization-7Gjf81u5hmw.mp4 5.2 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/37. M4 L3a 162 Real World Constraints Transaction Costs V2-HAif7xSh8z0.mp4 5.1 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.mp4 5.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 38 Proportion Intro V1-gMkMy0BeuaU.mp4 5.1 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/05. Model Complexity Graph-NnS0FJyVcDQ.mp4 5.1 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.mp4 5.1 MB
  • Part 02-Module 03-Lesson 06_Overlapping Labels/01. L6 01 HS Intro V2-zUOOluyHe_E.mp4 5.1 MB
  • Part 04-Module 01-Lesson 02_Vectors/01. Vectors 1-oPBz-MLVUHk.mp4 5.1 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/21. L1 24 Hedging Strategies V3-8bzw4ZMGpWU.mp4 5.1 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/01. M82 Intro V1-WQ95rYDyVSA.mp4 5.1 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/23. Error Function-V5kkHldUlVU.mp4 5.1 MB
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  • Part 03-Module 01-Lesson 02_Data Types and Operators/22. L2 07 Lists And Membership Operators II V3-3Nj-b-ZzqH8.mp4 5.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 21 Optional Explanation Part 2 V1-zpn4NQNQJh8.mp4 5.1 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.mp4 5.1 MB
  • Part 01-Module 02-Lesson 03_Regression/10. M2L3 09 Transforming Data V3-N8Fhq8wiQZU.mp4 5.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 66 Discussion V1-oTUWNw8X45M.mp4 5.0 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.mp4 5.0 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.mp4 5.0 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/03. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 5.0 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 5.0 MB
  • Part 01-Module 02-Lesson 05_Volatility/06. M2L5 06 Rolling Windows V3-4EuMKqeNXA0.mp4 5.0 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 10 Dispersion Solution V1-S5lzU1nAuhU.mp4 5.0 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/03. KALMAN Tracking Intro RENDER V2-C73G7vfVNQc.mp4 5.0 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/28. L1 32 Summary V1-Pt2sVftdwS0.mp4 5.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.mp4 5.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.mp4 5.0 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/09. M4 L3b 07 Winners And Losers Accelerated And Decelerated Gains And Losses V2-cdSdKl4uxVM.mp4 4.9 MB
  • Part 02-Module 01-Lesson 04_Feature Extraction/08. Embeddings For Deep Learning-gj8u1KG0H2w.mp4 4.9 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 09 Gini Solution V1-xCjkhgQDTu4.mp4 4.9 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 65 Rank Shap Solution V1-jkAxXatozUo.mp4 4.9 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.mp4 4.9 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/15. 11 SkipGram Negative V1-e7ZrzpyXNDs.mp4 4.9 MB
  • Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.9 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.mp4 4.9 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.mp4 4.9 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.mp4 4.9 MB
  • Part 01-Module 03-Lesson 02_ETFs/05. L2 07 ETF Sponsor V2-v5vfAP1nJ10.mp4 4.9 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/19. M4 L1B 18 EventDriven Factors V1-2mnwjChH2hg.mp4 4.9 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/04. Unstructured Text-OmwSdaec5vU.mp4 4.9 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/05. Unstructured Text-OmwSdaec5vU.mp4 4.9 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/06. Context-J-4pfu2w1C0.mp4 4.8 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/07. Context-J-4pfu2w1C0.mp4 4.8 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 22 Relative Returns V2-m4MvYRlyPoU.mp4 4.8 MB
  • Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.mp4 4.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/01. L7 Intro V1-tNGSsp6vUvY.mp4 4.8 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.mp4 4.8 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/03. 03. Role In AI Render-xm1TXnNe5Pw.mp4 4.8 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/09. M8l2 18 Linear Price Impact Part2 V2-aTNfxMjEg3w.mp4 4.8 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/18. M4 L3b 14 IVol Value And Idiosyncratic Volatility Overview V2-h7vamh2FPMs.mp4 4.8 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/06. 5 GettingRid ZeroLength V1-Hs6ithuvDJg.mp4 4.8 MB
  • Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.8 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/27. M4 L3a 13 Sharpe Ratio V4-W8nfg1fkloA.mp4 4.8 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/13. M1L6 12 Finding Alpha V1-r8lfWVhfQC0.mp4 4.7 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.mp4 4.7 MB
  • Part 01-Module 04-Lesson 01_Factors/08. M4 L1A 07 Rescale Part 1 V2-BcsxA0vy3jA.mp4 4.7 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/16. M4 L2A 27 Summary V1-rdqINNkTlqs.mp4 4.7 MB
  • Part 02-Module 03-Lesson 04_Random Forests/12. L4 17 HS Outro V2-oH7B6EyLE0k.mp4 4.7 MB
  • Part 01-Module 01-Lesson 06_Data Processing/08. M1L4 11 Survivor Bias V2-39MeCCw5ndM.mp4 4.7 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.mp4 4.7 MB
  • Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.mp4 4.7 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 16 Sector Intro Part 2 V1-DmYfsKS6kRM.mp4 4.7 MB
  • Part 03-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.mp4 4.7 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.mp4 4.7 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.mp4 4.7 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/06. M1L6 06 Trading Strategy V2-rrCHC20FkIc.mp4 4.6 MB
  • Part 01-Module 02-Lesson 04_Time Series Modeling/02. M2L4 02 Autoregressive Models V5-9jE7S4b-oIU.mp4 4.6 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.mp4 4.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 36 Algorithm Part 1 V2-4ko5-Ck-yCQ.mp4 4.6 MB
  • Part 01-Module 03-Lesson 05_Project 3 Smart Beta and Portfolio Optimization/01. MV 12 Transition To Project 03 V1-ClzlNlWqMQI.mp4 4.6 MB
  • Part 01-Module 03-Lesson 02_ETFs/03. L2 03 Commodity Futures V3-qvSubjxMGJ0.mp4 4.6 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/08. M1L3 10 Volume V3-DFp7kp0xRCo.mp4 4.6 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.mp4 4.6 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.mp4 4.6 MB
  • Part 05-Module 01-Lesson 02_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.mp4 4.6 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/07. L1 08 SP Index Categories V2-D3VGIvti71g.mp4 4.5 MB
  • Part 01-Module 04-Lesson 09_Project 4 Alpha Research and Factor Modeling/04. M4 03 Coming In Term II V1-2jF5J8MIdqc.mp4 4.5 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/41. M4 L3a 181 Quantile Analysis Part 1 V2-oT5GFbg0G8g.mp4 4.5 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/04. M4 L4 04 Regularization V4-fq-CanyDHuw.mp4 4.5 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/17. L1 19 Smart Beta V2-Rc9NEmNMzk8.mp4 4.5 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/10. Entropy Formula-iZiSYrOKvpo.mp4 4.5 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.5 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.mp4 4.5 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4 4.5 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/14. M2L6 20 Summary V2-wuzha8SU2jw.mp4 4.5 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/05. M4 L2A 16 Fama French Size Factor V2-94a2ugitC_E.mp4 4.4 MB
  • Part 03-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.mp4 4.4 MB
  • Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.mp4 4.4 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.mp4 4.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 29 Shapley Intro V1-yOfZDm99Vac.mp4 4.4 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.4 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.4 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/05. M2L2 04 Spotting Outliers In Raw Data V3-kFIB0YIW1TQ.mp4 4.4 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/48. M4 L3a 21 Its All Relative V2-VBcOrT7TuFA.mp4 4.4 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.mp4 4.4 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/13. M4 L2A 24 Categorical Variable Estimation V4-50hvTluqz3U.mp4 4.4 MB
  • Part 06-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.mp4 4.4 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.mp4 4.4 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/01. M1L6 01 Designing A Trading Strategy V4-O7c6bPXBUsU.mp4 4.4 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/04. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.4 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.4 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.mp4 4.4 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.mp4 4.4 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/16. M4 L2b 15 PCA As A Factor Model Pt 1 V3-4E3C5E-MmkI.mp4 4.4 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/07. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.4 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.4 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.mp4 4.4 MB
  • Part 01-Module 03-Lesson 02_ETFs/08. L2 10 Lower Operational Costs And Taxes V2-UlJusglK0h0.mp4 4.4 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/01. 矩阵介绍-Ugx3mldc0lE.mp4 4.4 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/09. L1 10 Market Cap Weighting V2-7qVVA5yLFnY.mp4 4.4 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/05. AIT M5L5 06 World Lists Searches V2-RutjcGh74Mw.mp4 4.3 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/10. L1 11 Adding Or Removing From An Index V2-_bWIZWa20j8.mp4 4.3 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.3 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 08 Gini Intro V1-_Ar4nlfUUEM.mp4 4.3 MB
  • Part 07-Module 01-Lesson 03_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4 4.3 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/26. Why Are Sampling Distributions Important-aDFDOCJKoH0.mp4 4.3 MB
  • Part 01-Module 01-Lesson 06_Data Processing/15. MV 06 Our Goal Is To Help You Meet Your Goals V1--pSppDzJRu8.mp4 4.3 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 09 Dispersion Intro Part 1 V2-xCGuuymx180.mp4 4.3 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.mp4 4.3 MB
  • Part 03-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.mp4 4.2 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/03. LSTM Basics-gjb68a4XsqE.mp4 4.2 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 13 Marketvol Intro V1-G03W42Z5RSo.mp4 4.2 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.2 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.2 MB
  • Part 02-Module 01-Lesson 04_Feature Extraction/02. Bag Of Words-A7M1z8yLl0w.mp4 4.2 MB
  • Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.mp4 4.2 MB
  • Part 01-Module 03-Lesson 02_ETFs/03. L2 02 Commodities V2-gc_GMqbCC2Q.mp4 4.2 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/08. M5 SC 3 Finding Metacharacters V1-RiSVD9E823Q.mp4 4.2 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.mp4 4.2 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.mp4 4.2 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/17. M4 L2A 11 Types Of Risk Models V1-SHj2VzJggAE.mp4 4.2 MB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/02. M2L6 02 Mean Reversion V5-zQ08lFcZa_A.mp4 4.2 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/12. M4 L2A 08 Variance Of 2 Stocks Part 1 V3-PlPusmuR20k.mp4 4.2 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/27. L2 04 Tuples V3-33xN-AbTMoc.mp4 4.2 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.1 MB
  • Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.mp4 4.1 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/03. L1 03 Indices Are Virtual Portfolios V2-oAd_szbBNWc.mp4 4.1 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/02. M4 L2b 02 Vector Two Ways V3-mlw6FnCUloU.mp4 4.1 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.mp4 4.1 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/09. M4 L2A 20 Fama French Risk Model V3-tepvGkpNKrI.mp4 4.1 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.mp4 4.1 MB
  • Part 01-Module 04-Lesson 01_Factors/07. M4 L1A 06 Demean Part 2 V2-aaj1QVsSCIs.mp4 4.1 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.mp4 4.1 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 4.1 MB
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  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.mp4 4.1 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/15. L1 17 Active Vs Passive V2-QzoHmUzJ5zw.mp4 4.1 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/01. M8L1 01 Intro V2-RjUIPy_robk.mp4 4.0 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.mp4 4.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.mp4 4.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 4.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 4.0 MB
  • Part 06-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.mp4 4.0 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/14. MV 13 Global Talent Is Equally Distributed V1-QwDJbbBl_48.mp4 4.0 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/01. M1L1 01 Welcome V1-W2R32yXgwcg.mp4 4.0 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 47 Marginal Solution V1-HINVjMBV6O8.mp4 4.0 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/05. M8l2 05 Holdings Dollars V1-GKA0cpKx0BE.mp4 4.0 MB
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  • Part 01-Module 04-Lesson 06_Alpha Factors/10. M4 L3a 07 Ranking Part 2 V2-uwPUV5LBhWY.mp4 4.0 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.mp4 4.0 MB
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  • Part 01-Module 02-Lesson 04_Time Series Modeling/03. M2L4 03 Moving Average Models V5-1FkCP_dwxjI.mp4 3.9 MB
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  • Part 06-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.mp4 3.9 MB
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  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/17. M4 L2b 16 PCA As A Factor Model Pt 2 V3-lcd-muqX5og.mp4 3.9 MB
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  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/26. M4 L3b 22 Summary V2-Tq8yVPEHxXs.mp4 3.9 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.mp4 3.8 MB
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  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/12. M4 L1B 11 How An Alpha Factor Becomes A Risk Factor Part 1 V3-p0cTudt8kXI.mp4 3.8 MB
  • Part 01-Module 01-Lesson 06_Data Processing/03. M1L4 04b Dividends V2-OVZw9tci55w.mp4 3.8 MB
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  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/06. M4 L2A 17 Fama French Size Factor V3-FXZuHsn0bx4.mp4 3.8 MB
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  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/11. Emotion as a Service-2jAP3rP3USM.mp4 3.8 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/04. M8l2 04 Time Offset Take2 V1-TYYV3MnhCP0.mp4 3.8 MB
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  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/02. RNN Vs LSTM-70MgF-IwAr8.mp4 3.8 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/14. M4 L1B 13 Momentum Or Reversal V3-izTAHVF6V_g.mp4 3.7 MB
  • Part 01-Module 03-Lesson 02_ETFs/04. L2 06 Hedging V3-4k1bdohhawI.mp4 3.7 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.mp4 3.7 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/03. L1 04 Indices Describe The Market V2-jNzwxE3el7I.mp4 3.7 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/13. PyTorch V2 Part 3 Solution 2 V1-ExyFG2MjsKs.mp4 3.7 MB
  • Part 01-Module 03-Lesson 02_ETFs/10. L2 12 Misaligned ETF Pricing V3-5-pBZ3fyv6I.mp4 3.7 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.7 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.7 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.mp4 3.7 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/01. M1L3 01 Intro V4-LE-4Xf8lzHk.mp4 3.7 MB
  • Part 01-Module 03-Lesson 02_ETFs/09. L2 11 Arbitrage V2-yp-CcGrMzYQ.mp4 3.7 MB
  • Part 01-Module 02-Lesson 04_Time Series Modeling/01. M2L4 01 Time Series Modeling V4-QeIu7GMZl20.mp4 3.7 MB
  • Part 05-Module 01-Lesson 03_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.mp4 3.7 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.mp4 3.7 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/01. M4 L2A 12 Time Series Risk Model Factor Variance V2-hjVBXeZmA0w.mp4 3.7 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.mp4 3.7 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/01. M4 L1B 01 Intro To Lesson V1-ff0paDNA75U.mp4 3.7 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/10. M4 L2A 06 Variance Of One Stock V3-rxaABg4wVZo.mp4 3.7 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 54 Test1 V1-6yMdxyiykeg.mp4 3.6 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/10. M4 L2A 21 Cross Sectional Risk Model V3-mpnRAt8qUus.mp4 3.6 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/05. M4 L2A 03 Factor Model Of Asset Return V2-7UnllxDmLj8.mp4 3.6 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/07. M4 L2A 04 Factor Model Of Portfolio Return V3-HEoPljS1wD0.mp4 3.6 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/10. Feature Extraction-UgENzCmfFWE.mp4 3.6 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/11. Feature Extraction-UgENzCmfFWE.mp4 3.6 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.6 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/15. M4 L2A 26 Fundamental Factors V2-fndhL2Tolac.mp4 3.6 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/27. M4 L1B 26 Summary V1-yuLQA24Thms.mp4 3.6 MB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/01. M4 L4 01 Intro V1-9NzZFszX2E4.mp4 3.6 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/01. Intro Arpan-MW5MWOLj064.mp4 3.6 MB
  • Part 02-Module 03-Lesson 06_Overlapping Labels/05. L6 06 HS Adjust Bag Size V4-BZbgljJnGYE.mp4 3.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 39 Proportion Solution V1-i03PYAy2ijE.mp4 3.6 MB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/07. 5 Subsampling Solution V1-YXruURuFD7g.mp4 3.6 MB
  • Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.mp4 3.6 MB
  • Part 06-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.mp4 3.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 55 Test2 V1-36-igThq7yI.mp4 3.6 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/07. Natural Language Processing-UQBxJzoCp-I.mp4 3.6 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/08. Natural Language Processing-UQBxJzoCp-I.mp4 3.6 MB
  • Part 03-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.mp4 3.5 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 3 V1-QOwy_XTIAes.mp4 3.5 MB
  • Part 02-Module 03-Lesson 06_Overlapping Labels/03. L6 03 HS The Problem V4-4nTtrMyP6zQ.mp4 3.5 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/05. L1 06 Growth Vs Value V2-ZCjre5YTD0s.mp4 3.5 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.mp4 3.5 MB
  • Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.mp4 3.5 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.mp4 3.5 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.mp4 3.5 MB
  • Part 01-Module 02-Lesson 01_Quant Workflow/05. M2L1 04 Anatomy Of A Strategy Part 1 V5-cnJK8c2zfq4.mp4 3.5 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.mp4 3.5 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/08. M8l2 10 Linear Impact V2-ntAIxAOzQQM.mp4 3.5 MB
  • Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4 3.5 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/14. PyTorch - Part 4-AEJV_RKZ7VU.mp4 3.5 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.mp4 3.5 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.5 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.5 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 01 Intro In The First Five V1-magg5AVJRVA.mp4 3.5 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.mp4 3.5 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.4 MB
  • Part 01-Module 01-Lesson 04_Stock Prices/01. M1L2 01 Stocks V6-23sv5ey0ySs.mp4 3.4 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Solution Part 1 V2-CliBAanDEhk.mp4 3.4 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.mp4 3.4 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.mp4 3.4 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.4 MB
  • Part 02-Module 01-Lesson 03_Text Processing/05. Tokenization-4Ieotbeh4u8.mp4 3.4 MB
  • Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/23. M4 L1B 22 Alternative Data V1-p6NxGZnkrdc.mp4 3.4 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
  • Part 01-Module 03-Lesson 02_ETFs/11. L2 14 Summary V1-E5br2PiH8kY.mp4 3.4 MB
  • Part 02-Module 05-Lesson 03_Attribution/05. M8l3 06 Performance Attribution V1-kLGjemiiPIs.mp4 3.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 46 Marginal Intro V1-RhDVEN3vHc8.mp4 3.3 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/07. M8l2 07 Tcost Part3 V2-ZFAZASS1m54.mp4 3.3 MB
  • Part 02-Module 03-Lesson 04_Random Forests/04. MLND SL DT 13 Random Forests MAIN V2-4xhjf6s_Pr0.mp4 3.3 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.mp4 3.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 32 Discussion Solution V1-pxmaMYOtNys.mp4 3.3 MB
  • Part 03-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.mp4 3.3 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/19. L1 21 Hedge Funds V4-AgGPqvDFTHw.mp4 3.3 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 48 Subsets Intro V1-ddoL_RcpZAc.mp4 3.3 MB
  • Part 02-Module 01-Lesson 03_Text Processing/04. Normalization-eOV2UUY8vtM.mp4 3.3 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/16. M4 L3a 09 Smoothing V2-mAfrjpZOf7Q.mp4 3.3 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4 3.3 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4 3.3 MB
  • Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.mp4 3.3 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/05. M1L3 08 Tick Data V4-2O0eSKmI6YQ.mp4 3.2 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.mp4 3.2 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/18. KALMAN QUIZ Gaussian Motion 01 RENDER V2-LFPT0R3VaPs.mp4 3.2 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/11. M8l2 13 Risk Factor Matrix V2-Obwjj-Fs2LM.mp4 3.2 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/20. M4 L3b 16 IVol Arbitrage Risk V3-rKtJ3iAYYns.mp4 3.2 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.mp4 3.2 MB
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.2 MB
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  • Part 06-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.mp4 3.2 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/18. L1 20 Mutual Funds V2-LgaylDkS92Y.mp4 3.2 MB
  • Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.mp4 3.1 MB
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  • Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.mp4 3.1 MB
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  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/09. Text Processing-pqheVyctkNQ.mp4 3.1 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/10. Text Processing-pqheVyctkNQ.mp4 3.1 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.mp4 3.1 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.mp4 3.1 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/12. M4 L2A 23 Categorical Factors V2-F76juAxHVIk.mp4 3.1 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.mp4 3.1 MB
  • Part 01-Module 04-Lesson 06_Alpha Factors/42. M4 L3a 182 Quantile Analysis Part 2 V3-NF18kx0sfBE.mp4 3.0 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/17. M4 L3b 13 Skewness And Momentum Conditional Factor V2-cMLTVZFKEm0.mp4 3.0 MB
  • Part 01-Module 03-Lesson 02_ETFs/03. L2 04 Commodity ETFs V2-UpgX6INJ6nU.mp4 3.0 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.mp4 3.0 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/07. L3 06 The Covariance Matrix And Quadratic Forms V1-as5lafBZ2CA.mp4 3.0 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.mp4 3.0 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/14. Character-Wise RNN-dXl3eWCGLdU.mp4 3.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 01-Module 01-Lesson 08_Momentum Trading/04. M1L6 04 Long And Short Positions V3-TCOFgM-hxkQ.mp4 3.0 MB
  • Part 02-Module 05-Lesson 03_Attribution/04. M8l3 05 Variance Decomposition V3-2V80v8sQl90.mp4 3.0 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.mp4 3.0 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.mp4 3.0 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.mp4 3.0 MB
  • Part 03-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.mp4 3.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 3.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 3.0 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/25. M4 L3b 21 IVol Generalizing The Volatility Factor V2-Lt1JPjKHPmk.mp4 3.0 MB
  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/09. AIT M5L5 99 Summary V1-ThLOv6gDyHI.mp4 3.0 MB
  • Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4 3.0 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.mp4 3.0 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.mp4 2.9 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/09. M1L3 12 Gaps In Market Data V3-jMT3VbUGiZI.mp4 2.9 MB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/07. 08. Computer Vision Pipeline-64hFcqhnNow.mp4 2.9 MB
  • Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4 2.9 MB
  • Part 01-Module 02-Lesson 04_Time Series Modeling/07. M2L4 08 Particle Filter V4-4KhDUAvwI74.mp4 2.9 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/10. M1L3 14 Markets In Different Timezones V3-wmmEpPM-HVs.mp4 2.9 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.mp4 2.9 MB
  • Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/18. PyTorch V2 Part 5 Solution 2 V1-3Py2SbtZLbc.mp4 2.9 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.mp4 2.9 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/09. M4 L2b 09 Writing It Down Pt 2 V2-TSH3hTAHsIg.mp4 2.9 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/07. M4 L2A 18 Fama French Value Factor V4-IcbsQ4QRGbs.mp4 2.9 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/19. M4 L3b 15 IVol Arbitrage And Efficient Pricing Of Stocks V3-7Fqe5DP6iG8.mp4 2.9 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.mp4 2.9 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.mp4 2.9 MB
  • Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4 2.9 MB
  • Part 01-Module 01-Lesson 06_Data Processing/02. M1L4 02 Market Data V5-9aEp374GsgQ.mp4 2.9 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/09. DL 08 AND And OR Perceptrons-Y-ImuxNpS40.mp4 2.9 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/09. DL 08 AND And OR Perceptrons-Y-ImuxNpS40.mp4 2.9 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/01. 01 Intro-4C4PuJANIdE.mp4 2.9 MB
  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.mp4 2.9 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/08. M4 L2A 05 Covariance Matrix Of Factors V3-llA1A0vjSuI.mp4 2.9 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/05. KALMAN Gaussian Intro RENDER 1 1 V3-S2v1CExswT4.mp4 2.8 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/08. M4 L2A 19 Fama French SMB And HML V2-fnncnimScFc.mp4 2.8 MB
  • Part 02-Module 03-Lesson 06_Overlapping Labels/04. L6 05 HS Subsample Rows V4-IAl8kotkzYg.mp4 2.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 11 Node Importance Solution V1-Il0GsMMyTcM.mp4 2.8 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.mp4 2.8 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.mp4 2.8 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4 2.8 MB
  • Part 02-Module 03-Lesson 01_Overview/03. Types Of Machine Learning Supervised V1-iGTuWwR6kZE.mp4 2.8 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 40 Wrapper Intro V1-IfwRHDSxwPs.mp4 2.8 MB
  • Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.mp4 2.8 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/05. M4 L2b 05 Translating Between Bases V4-lrE4VOJ2RCA.mp4 2.8 MB
  • Part 01-Module 02-Lesson 05_Volatility/13. M2L5 13 Breakout Strategies V4-9eamk40DMu0.mp4 2.8 MB
  • Part 07-Module 01-Lesson 02_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.7 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/03. M4 L2A 02 Motivation For Risk Factor Model V2-jAQRjxK8PyQ.mp4 2.7 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/13. L4 14 Recap V1-e3qJYCQfJD0.mp4 2.7 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.mp4 2.7 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.mp4 2.7 MB
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  • Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.mp4 2.7 MB
  • Part 03-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.mp4 2.7 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.mp4 2.7 MB
  • Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.mp4 2.7 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/07. M8l2 07 Tcost Part2 V1-mWMFwCkWEFk.mp4 2.7 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/26. KALMAN QUIZ Kalman Prediction V1-d8Gx4-RghD0.mp4 2.7 MB
  • Part 04-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.mp4 2.6 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.mp4 2.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 60 Rank Sklearn V1-qZc-VBI4wY0.mp4 2.6 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.mp4 2.6 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/10. M4 L2b 10 Writing It Down Pt 3 V3-kSl0j4QIMIU.mp4 2.6 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 04b Project Reviews V1-KJbx9f9VKJE.mp4 2.6 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.mp4 2.6 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.mp4 2.6 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/10. M7L5 24 Targets Solution V2-rC27Xoeyes0.mp4 2.6 MB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.mp4 2.6 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/16. L3 13 Summary V1-I7XKJf8t_0s.mp4 2.6 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 25 CaseC Intro V1-C6i12sqcgtY.mp4 2.6 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.mp4 2.6 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.mp4 2.5 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 17 Sector Solution V1-I9iFbxzIu60.mp4 2.5 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 43 Prediction Solution Part 2 V1-y-vY_23kSU0.mp4 2.5 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/12. M4 L2b 13 Principal Components V3-XtecKk58CLs.mp4 2.5 MB
  • Part 02-Module 01-Lesson 03_Text Processing/03. Capturing Text Data-Z4mnMN1ApG4.mp4 2.5 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.mp4 2.5 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.mp4 2.5 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/06. M1L3 09 Open High Low Close V4-FgNY4YgVWFk.mp4 2.5 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/06. Accuracy-s6SfhPTNOHA.mp4 2.5 MB
  • Part 02-Module 03-Lesson 04_Random Forests/05. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.5 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/09. Experiment II-fq4eO7CybA4.mp4 2.4 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Part 3 V1-xwBdEXXTu1s.mp4 2.4 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.mp4 2.4 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/15. Sequence-Batching-Z4OiyU0Cldg.mp4 2.4 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.mp4 2.4 MB
  • Part 01-Module 03-Lesson 03_Portfolio Risk and Return/03. L3 03 Portfolio Mean V3-vozlctvug7I.mp4 2.4 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 13 Feature Importance Solution V1-C0_ngrOk-TA.mp4 2.4 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.4 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.4 MB
  • Part 02-Module 01-Lesson 04_Feature Extraction/01. Feature Extraction-Bd6TJB8eVLQ.mp4 2.4 MB
  • Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.mp4 2.4 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/04. M1L3 04 Liquidity V4-KNVQeH6Y_YA.mp4 2.4 MB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.mp4 2.4 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.mp4 2.4 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/11. 06 Precision SC V1-q2wVorBfefU.mp4 2.4 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.mp4 2.3 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/10. Answer False Negatives And Positives-KOytJL1lvgg.mp4 2.3 MB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/05. Learn Gate-aVHVI7ovbHY.mp4 2.3 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4 2.3 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.mp4 2.3 MB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.mp4 2.3 MB
  • Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.mp4 2.3 MB
  • Part 01-Module 01-Lesson 07_Stock Returns/01. M1L5 01 Intro V2-mE8OOxkgzy8.mp4 2.3 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.mp4 2.3 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 2 V1--I-jF0Ikli0.mp4 2.3 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/08. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.3 MB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/01. L4 01 Intro V1-CtIcmmR0YTs.mp4 2.3 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.mp4 2.3 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/03. M4 L2A 14 Time Series Risk Model Specific Variance V2-I0uJLfh_OgQ.mp4 2.3 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.mp4 2.3 MB
  • Part 02-Module 03-Lesson 05_Feature Engineering/10. M7L5 23 Targets Intro V1-yaKHIvm7cXY.mp4 2.3 MB
  • Part 02-Module 05-Lesson 01_Intro to Backtesting/11. M8L1 15 Outro V1-mFk_HYJLF1w.mp4 2.3 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/02. M4 L2A 13 Time Series Risk Model Factor Exposure V4-WPBSMptBrfw.mp4 2.3 MB
  • Part 02-Module 03-Lesson 02_Decision Trees/15. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.3 MB
  • Part 07-Module 01-Lesson 04_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.3 MB
  • Part 02-Module 01-Lesson 03_Text Processing/08. Part-of-Speech Tagging-WFEu8bXI5OA.mp4 2.3 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/08. When Accuracy Wont Work-r0-O-gIDXZ0.mp4 2.3 MB
  • Part 01-Module 02-Lesson 03_Regression/09. M2L3 08 Heteroskedasticity V2-wias9OZ1tU4.mp4 2.3 MB
  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.mp4 2.3 MB
  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/12. 07 Recall SC V1-0n5wUZiefkQ.mp4 2.3 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.mp4 2.2 MB
  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/03. Grammar-Jw3dA7xmoQ4.mp4 2.2 MB
  • Part 10-Module 01-Lesson 01_Intro to NLP/04. Grammar-Jw3dA7xmoQ4.mp4 2.2 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/15. Momentum-r-rYz_PEWC8.mp4 2.2 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.mp4 2.2 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.mp4 2.2 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/14. M4 L2A 25 Specific Variance V2-JwA9g3NBglE.mp4 2.2 MB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/14. KALMAN QUIZ Parameter Update 01 RENDER V3-UUXETqShme4.mp4 2.2 MB
  • Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.mp4 2.2 MB
  • Part 01-Module 01-Lesson 06_Data Processing/01. M1L4 01 Stock Data V2-sN0_IqmMGGA.mp4 2.2 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/01. M2L2 01 Intro V1-OGx1aYHMgbs.mp4 2.2 MB
  • Part 01-Module 02-Lesson 03_Regression/17. M2L3 15 Summary V1-n2VxcEcw0GY.mp4 2.2 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.mp4 2.2 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.mp4 2.2 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.mp4 2.2 MB
  • Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.mp4 2.2 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.2 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.2 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.mp4 2.2 MB
  • Part 01-Module 02-Lesson 02_Outliers and Filtering/09. M2L2 08 Generating Robust Trading Signals V3-1ikkZmVkjl0.mp4 2.2 MB
  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.mp4 2.2 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/03. Exemplo de classificação-Dh625piH7Z0.mp4 2.2 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/03. Exemplo de classificação-Dh625piH7Z0.mp4 2.2 MB
  • Part 02-Module 01-Lesson 05_Financial Statements/26. AIT M5L4B 99 Summary V1-NgIufQFEHps.mp4 2.2 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.mp4 2.2 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/22. L1 25 Net Asset Value V2-hBnY2DmEFo4.mp4 2.2 MB
  • Part 06-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.mp4 2.2 MB
  • Part 02-Module 01-Lesson 04_Feature Extraction/03. TF-IDF-XZBiBIRcACE.mp4 2.2 MB
  • Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.mp4 2.1 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.mp4 2.1 MB
  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/08. M4 L3b 06 Winners And Losers In Momentum Investing V2-84ygzbLENbE.mp4 2.1 MB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.mp4 2.1 MB
  • Part 02-Module 02-Lesson 02_Training Neural Networks/03. Testing-EeBZpb-PSac.mp4 2.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 56 Test3 V1-W_N9wjVnfBk.mp4 2.1 MB
  • Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4 2.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 63 Local Global V1-8JiJ3R2F3Ng.mp4 2.1 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 2.1 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 2.1 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/02. M1L1 02 Interview W Jonathan V1-AeranuDRL7k.mp4 2.1 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/19. M4 L2b 19 Outro V1-nfVnAkndJCY.mp4 2.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 30 Shapley Solution V1-YmCSCA8Psgk.mp4 2.1 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/13. M4 L2A 09 Variance Of 2 Stocks Part 2 V4-tSMutw0f6OE.mp4 2.1 MB
  • Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/12. M8l2 14 N By N Matrix V3-qYAWhI8hk7U.mp4 2.1 MB
  • Part 06-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.mp4 2.1 MB
  • Part 01-Module 01-Lesson 05_Market Mechanics/11. M1L3 15 Outro V2-XVvfToYCsmo.mp4 2.1 MB
  • Part 02-Module 01-Lesson 03_Text Processing/07. Stop Word Removal-WAU_Ij0GJbw.mp4 2.1 MB
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 42 Prediction Intro Part 1 V2-MLAnUO0BSr0.mp4 2.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 2.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 2.0 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.mp4 2.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 2.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 2.0 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 23 Absolute Returns V3-wbb6WSyXLdU.mp4 2.0 MB
  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/11. M4 L2b 11 Writing It Down Pt 4 V3-7XO-syqIpCE.mp4 2.0 MB
  • Part 02-Module 03-Lesson 01_Overview/04. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.mp4 2.0 MB
  • Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.mp4 2.0 MB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/08. L1 09 Price Weighting V2-2SFbwJ19NhA.mp4 2.0 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.mp4 2.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.mp4 2.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.mp4 2.0 MB
  • Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.mp4 2.0 MB
  • Part 01-Module 04-Lesson 03_Risk Factor Models/11. M4 L2A 07 Taking Constants Out Of Variance And Covariance Optional V3-M9R9870m_o0.mp4 2.0 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 2.0 MB
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 2.0 MB
  • Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.mp4 1.9 MB
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  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/11. M4 L2A 22 Cross Sectional Risk Model A Different Approach V2-LauZ7h4bgKE.mp4 1.9 MB
  • Part 07-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.mp4 1.9 MB
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.mp4 1.9 MB
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  • Part 02-Module 03-Lesson 03_Model Testing and Evaluation/16. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4 1.8 MB
  • Part 01-Module 03-Lesson 02_ETFs/10. L2 13 Realigning ETF Share Prices V2-aRXJxjQQSiI.mp4 1.8 MB
  • Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.mp4 1.8 MB
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  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-09-21-at-11.36.43-am.png 1.8 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.7 MB
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  • Part 01-Module 04-Lesson 01_Factors/10. M4 L1A 09 Overview For Standardizing A Factor V3-0clT0lnrTrU.mp4 1.6 MB
  • Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 04 Study Groups V1-vmjk1EKR6mM.mp4 1.6 MB
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.mp4 1.6 MB
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  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/arpan-shocked.png 1.4 MB
  • Part 01-Module 03-Lesson 02_ETFs/07. L2 09 Redeeming Shares V3-ZSVgU7DBarc.mp4 1.4 MB
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.mp4 1.4 MB
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  • Part 01-Module 02-Lesson 04_Time Series Modeling/09. M2L4 11 Outro V1-6sheR92KUU8.mp4 1.4 MB
  • Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.mp4 1.4 MB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/04. M4 L2A 15 Time Series Risk Model V2-Lz3RMLmov8o.mp4 1.4 MB
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.4 MB
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function - Artificial Intelligence for Robotics-DV2cX9W0tT8.mp4 1.3 MB
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  • Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.mp4 1.1 MB
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  • Part 07-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4 1.0 MB
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  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/redacted-linkedinresults.png 236.3 kB
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  • Part 06-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.th.vtt 3.6 kB
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  • Part 01-Module 04-Lesson 06_Alpha Factors/30. M4 L3A 141 Ranked Information Coefficient Part 1 V4-_huNulOIuB0.en.vtt 3.6 kB
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  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.en.vtt 3.5 kB
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  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/02. M2L6 02 Mean Reversion V5-zQ08lFcZa_A.en.vtt 3.4 kB
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  • Part 07-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt 3.4 kB
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/02. Introduction-XZL934YQ-FQ.ru.vtt 3.4 kB
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  • Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.ja.vtt 3.4 kB
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.ru.vtt 3.3 kB
  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/01. MV 11 Intro To Module 03 Difficulties In Learning V1-kqjFkUVZwEc.en.vtt 3.3 kB
  • Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.en.vtt 3.3 kB
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  • Part 02-Module 05-Lesson 03_Attribution/04. M8l3 05 Variance Decomposition V3-2V80v8sQl90.en.vtt 3.3 kB
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  • Part 01-Module 02-Lesson 02_Outliers and Filtering/06. M2L2 05 Handling Outliers In Raw Data V3-3l6kQZqlVJA.en.vtt 3.3 kB
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  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.en.vtt 3.3 kB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/01. M4 L2A 12 Time Series Risk Model Factor Variance V2-hjVBXeZmA0w.en.vtt 3.3 kB
  • Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/04. MV 10 Transition From Project 02 Int V1-DYjOsL3VYfY.en.vtt 3.3 kB
  • Part 07-Module 01-Lesson 03_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.en.vtt 3.3 kB
  • Part 07-Module 01-Lesson 02_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.en.vtt 3.3 kB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.es-MX.vtt 3.3 kB
  • Part 02-Module 02-Lesson 05_Embeddings Word2Vec/15. 11 SkipGram Negative V1-e7ZrzpyXNDs.zh-CN.vtt 3.3 kB
  • Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.pt-BR.vtt 3.3 kB
  • Part 02-Module 03-Lesson 04_Random Forests/05. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.en.vtt 3.3 kB
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  • Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.ar.vtt 3.3 kB
  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/12. M4 L2A 23 Categorical Factors V2-F76juAxHVIk.en.vtt 3.3 kB
  • Part 03-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.pt-BR.vtt 3.3 kB
  • Part 06-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.zh-CN.vtt 3.3 kB
  • Part 06-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.pt-BR.vtt 3.2 kB
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  • Part 02-Module 01-Lesson 05_Financial Statements/22. M5 SC 13 Searching The Parse Tree Part 2 V1-WS_bkGCk7qk.en.vtt 3.2 kB
  • Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.pt-BR.vtt 3.2 kB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.en.vtt 3.2 kB
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/05. KALMAN Gaussian Intro RENDER 1 1 V3-S2v1CExswT4.en.vtt 3.2 kB
  • Part 07-Module 01-Lesson 03_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.pt-BR.vtt 3.2 kB
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt 3.2 kB
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  • Part 06-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.en.vtt 3.2 kB
  • Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/04. M4 L4 04 Regularization V4-fq-CanyDHuw.en.vtt 3.2 kB
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  • Part 06-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.en.vtt 3.2 kB
  • Part 01-Module 03-Lesson 04_Portfolio Optimization/02. L4 02 What Is Optimization V2-ISRlP1GeOjU.en.vtt 3.2 kB
  • Part 03-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.pt-BR.vtt 3.2 kB
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  • Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/06. M4 L3b 05 Overnight Returns Methods Quantile Analysis V3-4Js3mghq2mU.en.vtt 2.4 kB
  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/05. 05. Emotional Intelligence-D_LzJsJH5qk.en.vtt 2.4 kB
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  • Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/11. M4 L2A 22 Cross Sectional Risk Model A Different Approach V2-LauZ7h4bgKE.en.vtt 2.3 kB
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  • Part 09-Module 01-Lesson 01_Intro to Computer Vision/02. 02. What Is Vision-_99V1rUNFa4.zh-CN.vtt 1.8 kB
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  • Part 06-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.zh-CN.vtt 1.8 kB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.zh-CN.vtt 1.8 kB
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  • Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 22 Relative Returns V2-m4MvYRlyPoU.en.vtt 1.8 kB
  • Part 03-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.en.vtt 1.8 kB
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  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.pt-BR.vtt 1.8 kB
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  • Part 02-Module 05-Lesson 01_Intro to Backtesting/05. M8L1 10 Overtrading V2-0cdGLRDI_Sk.en.vtt 1.8 kB
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  • Part 06-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.zh-CN.vtt 1.8 kB
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  • Part 06-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.pt-BR.vtt 1.8 kB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.zh-CN.vtt 1.8 kB
  • Part 02-Module 01-Lesson 01_Welcome To Term II/02. AITND TII 02 Overview Of Term 2 V1-dVz-lVGvadY.en.vtt 1.8 kB
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.zh-CN.vtt 1.8 kB
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  • Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.ar.vtt 1.8 kB
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  • Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/04. Unstructured Text-OmwSdaec5vU.en.vtt 1.8 kB
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  • Part 10-Module 01-Lesson 01_Intro to NLP/09. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.en.vtt 1.8 kB
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  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.pt-BR.vtt 1.8 kB
  • Part 01-Module 03-Lesson 02_ETFs/03. L2 05 International ETFs V2-OL2p8S-82mY.en.vtt 1.8 kB
  • Part 06-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.en.vtt 1.8 kB
  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/08. LSTM 7 Use Gate-5Ifolm1jTdY.pt-BR.vtt 1.8 kB
  • Part 01-Module 04-Lesson 06_Alpha Factors/27. M4 L3a 13 Sharpe Ratio V4-W8nfg1fkloA.en.vtt 1.8 kB
  • Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.en.vtt 1.8 kB
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  • Part 02-Module 03-Lesson 01_Overview/04. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.pt-BR.vtt 1.6 kB
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  • Part 02-Module 02-Lesson 04_Recurrent Neural Networks/04. LSTM Architecture-ycwthhdx8ws.zh-CN.vtt 1.4 kB
  • Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/14. M2L6 20 Summary V2-wuzha8SU2jw.en.vtt 1.4 kB
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.ar.vtt 1.4 kB
  • Part 06-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.zh-CN.vtt 1.4 kB
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.en.vtt 1.4 kB
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  • Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/19. M4 L2b 19 Outro V1-nfVnAkndJCY.en.vtt 700 Bytes
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  • Part 02-Module 01-Lesson 04_Feature Extraction/10. NLP Summary-B9ul8fsQYOA.en.vtt 688 Bytes
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  • Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.en.vtt 617 Bytes
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  • Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.en.vtt 613 Bytes
  • Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 44 Weight Intro V1-1cautGeQWDE.en.vtt 612 Bytes
  • Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.es-ES.vtt 611 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.zh-CN.vtt 609 Bytes
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
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  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
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  • Part 02-Module 03-Lesson 06_Overlapping Labels/06. L6 08 HS Ensemble Models Trained On Nonoverlapping Periods V5-YiXkL-Ts67I.en.vtt 604 Bytes
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  • Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt 601 Bytes
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  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.ja-JP.vtt 599 Bytes
  • Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.hr.vtt 599 Bytes
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  • Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.en.vtt 598 Bytes
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  • Part 07-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt 590 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.pt-BR.vtt 589 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.ja.vtt 587 Bytes
  • Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.en.vtt 586 Bytes
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
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  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
  • Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.pt-BR.vtt 583 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt 583 Bytes
  • Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.zh-CN.vtt 582 Bytes
  • Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.pt-BR.vtt 582 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-Hans.vtt 579 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.zh-CN.vtt 572 Bytes
  • Part 06-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.en.vtt 572 Bytes
  • Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.it.vtt 571 Bytes
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  • Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.zh-CN.vtt 569 Bytes
  • Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.en.vtt 569 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.pt-BR.vtt 567 Bytes
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  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.es-ES.vtt 566 Bytes
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  • Part 02-Module 01-Lesson 06_Basic NLP Analysis/09. AIT M5L5 99 Summary V1-ThLOv6gDyHI.en.vtt 563 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ar.vtt 562 Bytes
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.ja-JP.vtt 561 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.es-ES.vtt 560 Bytes
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  • Part 07-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt 558 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.ja.vtt 557 Bytes
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-CN.vtt 556 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.zh-CN.vtt 554 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.th.vtt 554 Bytes
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.ja-JP.vtt 551 Bytes
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 Bytes
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ar.vtt 549 Bytes
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.th.vtt 548 Bytes
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ar.vtt 544 Bytes
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.th.vtt 541 Bytes
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  • Part 06-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.pt-BR.vtt 541 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.en.vtt 540 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt 540 Bytes
  • Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.hr.vtt 539 Bytes
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 Bytes
  • Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.zh-CN.vtt 538 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.es-ES.vtt 538 Bytes
  • Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.zh-CN.vtt 537 Bytes
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  • Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.ar.vtt 534 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ar.vtt 533 Bytes
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.es-ES.vtt 533 Bytes
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/11. KALMAN QUIZ Shifting The Mean 02 RENDER V1-L8vNIKvpJ1s.zh-CN.vtt 533 Bytes
  • Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.ja.vtt 532 Bytes
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  • Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.zh-CN.vtt 532 Bytes
  • Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.zh-CN.vtt 532 Bytes
  • Part 01-Module 02-Lesson 05_Volatility/14. M2L5 15 Outro V1-FMXL37CkTgg.en.vtt 531 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ja.vtt 531 Bytes
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  • Part 02-Module 05-Lesson 01_Intro to Backtesting/11. M8L1 15 Outro V1-mFk_HYJLF1w.en.vtt 510 Bytes
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.it.vtt 476 Bytes
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  • README.txt 454 Bytes
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  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 Bytes
  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ar.vtt 437 Bytes
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  • Part 01-Module 02-Lesson 04_Time Series Modeling/09. M2L4 11 Outro V1-6sheR92KUU8.en.vtt 425 Bytes
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  • Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.en.vtt 423 Bytes
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  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
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  • Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.pt-BR.vtt 411 Bytes
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  • Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.pt-BR.vtt 401 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ar.vtt 399 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.th.vtt 398 Bytes
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.en.vtt 397 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ar.vtt 396 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.th.vtt 393 Bytes
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.pt-BR.vtt 391 Bytes
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ja.vtt 390 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.zh-CN.vtt 390 Bytes
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.zh-CN.vtt 389 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.zh-CN.vtt 389 Bytes
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  • Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt 385 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt 385 Bytes
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  • Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.zh-CN.vtt 379 Bytes
  • Part 02-Module 05-Lesson 03_Attribution/01. M8L4 01 Intro V1-sIh09ScQXCQ.en.vtt 378 Bytes
  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.pt-BR.vtt 377 Bytes
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.ja-JP.vtt 376 Bytes
  • Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.it.vtt 375 Bytes
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  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ja.vtt 371 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.en.vtt 370 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt 370 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.es-ES.vtt 367 Bytes
  • Part 02-Module 05-Lesson 03_Attribution/09. M8L4 010 Outro V1-PNnSfMm-7-s.en.vtt 366 Bytes
  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.es-ES.vtt 366 Bytes
  • Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ar.vtt 364 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.en.vtt 364 Bytes
  • Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.pt-BR.vtt 362 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt 361 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.pt-BR.vtt 360 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.en.vtt 358 Bytes
  • Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 Bytes
  • Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ja.vtt 357 Bytes
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.zh-CN.vtt 355 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.es-ES.vtt 355 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.zh-CN.vtt 355 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt 355 Bytes
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  • Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.th.vtt 354 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.en.vtt 353 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.en.vtt 351 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.es-ES.vtt 350 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ja.vtt 350 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.pt-BR.vtt 349 Bytes
  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.en.vtt 349 Bytes
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.zh-CN.vtt 349 Bytes
  • Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.zh-CN.vtt 348 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.it.vtt 341 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.ar.vtt 341 Bytes
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.ja.vtt 340 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.en.vtt 339 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.pt-BR.vtt 339 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.it.vtt 338 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ar.vtt 336 Bytes
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  • Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.pt-BR.vtt 335 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.es-ES.vtt 332 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ja.vtt 332 Bytes
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.pt-BR.vtt 332 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt 332 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ja.vtt 331 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.zh-CN.vtt 328 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.en.vtt 325 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ar.vtt 325 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.zh-CN.vtt 320 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.hr.vtt 319 Bytes
  • Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.ar.vtt 319 Bytes
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.ja.vtt 319 Bytes
  • Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.es-ES.vtt 318 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.pt-BR.vtt 317 Bytes
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.ja-JP.vtt 317 Bytes
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.zh-CN.vtt 316 Bytes
  • Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.it.vtt 315 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ja.vtt 315 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ar.vtt 315 Bytes
  • Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt 315 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.it.vtt 314 Bytes
  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.zh-CN.vtt 314 Bytes
  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.es-ES.vtt 314 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.en.vtt 313 Bytes
  • Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.ar.vtt 312 Bytes
  • Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.hr.vtt 312 Bytes
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  • Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.zh-CN.vtt 311 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.en.vtt 310 Bytes
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ja.vtt 305 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ja.vtt 305 Bytes
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  • Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.en.vtt 297 Bytes
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  • Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt 277 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.ar.vtt 153 Bytes
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  • Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.ar.vtt 152 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.th.vtt 130 Bytes
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  • Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.ja.vtt 126 Bytes
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-Hans.vtt 119 Bytes
  • Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.en.vtt 119 Bytes
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  • Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.zh-CN.vtt 118 Bytes
  • Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.ar.vtt 118 Bytes
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  • Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ja.vtt 116 Bytes
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