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Stanford University

Statistical Learning with Python

via YouTube Video playlist

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Overview

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani and Taylor (Springer, 2023). The pdf for this book is available for free on the book website.

What You'll Learn:

Overview of statistical learning

Linear regression

Classification

Resampling methods

Linear model selection and regularization

Moving beyond linearity

Tree-based methods

Support vector machines

Deep learning

Survival modeling

Unsupervised learning

Multiple testing

Videos in this playlist 80

  1. Statistical Learning: 1.1 Opening Remarks
  2. Statistical Learning: 8 Years Later (Second Edition of the Course)
  3. Statistical Learning I Introducing Jonathan - Third Edition of the Course I 2023
  4. Statistical Learning: 1.2 Examples and Framework
  5. Statistical Learning: 2.1 Introduction to Regression Models
  6. Statistical Learning: 2.2 Dimensionality and Structured Models
  7. Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
  8. Statistical Learning: 2.4 Classification
  9. Statistical Learning: 2.Py Setting Up Python I 2023
  10. Statistical Learning: 2.Py Data Types, Arrays, and Basics I 2023
  11. Statistical Learning: 2.Py.3 Graphics I 2023
  12. Statistical Learning: 2.Py Indexing and Dataframes I 2023
  13. Statistical Learning: 3.1 Simple linear regression
  14. Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
  15. Statistical Learning: 3.3 Multiple Linear Regression
  16. Statistical Learning: 3.4 Some important questions
  17. Statistical Learning: 3.5 Extensions of the Linear Model
  18. Statistical Learning: 3.Py Linear Regression and statsmodels Package I 2023
  19. Statistical Learning: 3.Py Multiple Linear Regression Package I 2023
  20. Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 2023
  21. Statistical Learning: 4.1 Introduction to Classification Problems
  22. Statistical Learning: 4.2 Logistic Regression
  23. Statistical Learning: 4.3 Multivariate Logistic Regression
  24. Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass
  25. Statistical Learning: 4.5 Discriminant Analysis
  26. Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)
  27. Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)
  28. Statistical Learning: 4.8 Generalized Linear Models
  29. Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
  30. Statistical Learning: 4.Py Logistic Regression I 2023
  31. Statistical Learning: 4.Py Linear Discriminant Analysis (LDA) I 2023
  32. Statistical Learning: 4.Py K-Nearest Neighbors (KNN) I 2023
  33. Statistical Learning: 5.1 Cross Validation
  34. Statistical Learning: 5.2 K-fold Cross Validation
  35. Statistical Learning: 5.3 Cross Validation the wrong and right way
  36. Statistical Learning: 5.4 The Bootstrap
  37. Statistical Learning: 5.5 More on the Bootstrap
  38. Statistical Learning: 5.Py Cross-Validation I 2023
  39. Statistical Learning: 5.Py Bootstrap I 2023
  40. Statistical Learning: 6.1 Introduction and Best Subset Selection
  41. Statistical Learning: 6.2 Stepwise Selection
  42. Statistical Learning: 6.3 Backward stepwise selection
  43. Statistical Learning: 6.4 Estimating test error
  44. Statistical Learning: 6.5 Validation and cross validation
  45. Statistical Learning: 6.6 Shrinkage methods and ridge regression
  46. Statistical Learning: 6.7 The Lasso
  47. Statistical Learning: 6.8 Tuning parameter selection
  48. Statistical Learning: 6.9 Dimension Reduction Methods
  49. Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares
  50. Statistical Learning: 6.Py Stepwise Regression I 2023
  51. Statistical Learning: 6.Py Ridge Regression and the Lasso I 2023
  52. Statistical Learning: 7.1 Polynomials and Step Functions
  53. Statistical Learning: 7.2 Piecewise Polynomials and Splines
  54. Statistical Learning: 7.3 Smoothing Splines
  55. Statistical Learning: 7.4 Generalized Additive Models and Local Regression
  56. Statistical Learning: 7.Py Polynomial Regressions and Step Functions I 2023
  57. Statistical Learning: 7.Py Splines I 2023
  58. Statistical Learning: 7.Py Generalized Additive Models (GAMs) I 2023
  59. Statistical Learning: 8.1 Tree based methods
  60. Statistical Learning: 8.2 More details on Trees
  61. Statistical Learning: 8.3 Classification Trees
  62. Statistical Learning: 8.4 Bagging
  63. Statistical Learning: 8.5 Boosting
  64. Statistical Learning: 8.6 Bayesian Additive Regression Trees
  65. Statistical Learning: 8.Py Tree-Based Methods I 2023
  66. Statistical Learning: 9.1 Optimal Separating Hyperplane
  67. Statistical Learning: 9.2.Support Vector Classifier
  68. Statistical Learning: 9.3 Feature Expansion and the SVM
  69. Statistical Learning: 9.4 Example and Comparison with Logistic Regression
  70. Statistical Learning: 9.Py Support Vector Machines I 2023
  71. Statistical Learning: 9.Py ROC Curves I 2023
  72. Statistical Learning: 10.1 Introduction to Neural Networks
  73. Statistical Learning: 10.2 Convolutional Neural Networks
  74. Statistical Learning: 10.3 Document Classification
  75. Statistical Learning: 10.4 Recurrent Neural Networks
  76. Statistical Learning: 10.5 Time Series Forecasting
  77. Statistical Learning: 10.6 Fitting Neural Networks
  78. Statistical Learning: 10.7 Interpolation and Double Descent
  79. Statistical Learning: 10.Py Single Layer Model: Hitters Data I 2023
  80. Statistical Learning: 10.Py Multilayer Model: MNIST Digit Data I 2023

Advantages and disadvantages

Advantages

  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
  • Taught by Stanford University, one of the strongest names in its field.
  • 345,714 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (80 videos) you can see before you start.

Disadvantages

  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • Graduate level: needs linear algebra, probability and programming first.
  • No certificate.
  • No graded assignments or feedback.

Some points apply to every course of this kind; see how we rank.

Free

  • Free: Watch on YouTube, no account needed.
  • Certificate: None. Code or take notes along to make it stick.

Stanford University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (97/100).

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