Introduction to Statistical Learning: http://www.StatLearning.com

The PDF book is available for free on the author's site above.

Working through the book and the labs.

Videos

Our solutions:

• Chapter 2 Solutions
• Chapter 3 Solutions
• Chapter 4 Solutions
• Chapter 5 Solutions
• Chapter 6 Solutions
• Chapter 7 Solutions
• Chapter 8 Solutions
• Chapter 9 Solutions
• Chapter 10 Solutions

• Chapter 1: Introduction
• Chapter 2: Statistical Learning
• Chapter 3: Linear Regression
• Chapter 4: Classification
• Chapter 5: Resampling Methods
• Chapter 6: Linear Model Selection and Regularization
• Chapter 7: Moving Beyond Linearity
• Chapter 8: Tree-Based Methods
• Chapter 9: Support Vector Machines
• Chapter 10: Unsupervised Learning

• http://blog.princehonest.com/stat-learning/
  • https://github.com/asadoughi/stat-learning/
• https://github.com/ppaquay/IntroStatLearning
• https://github.com/jilmun/ISLR
• https://github.com/KamranMK/ISLR-Solutions
• https://laptrinhx.com/solutions-to-labs-and-excercises-from-an-introduction-to-statistical-learning-as-jupyter-notebooks-1723363054/
• https://altaf-ali.github.io/ISLR/index.html
• https://github.com/evertonjlima/Notebooks
• https://waxworksmath.com/Authors/G_M/James/james.html

• https://github.com/tdpetrou/Machine-Learning-Books-With-Python/tree/master/Introduction%20to%20Statistical%20Learning
• https://www.franzoni.eu/machine-learning-a-sound-primer/
• https://github.com/JWarmenhoven/ISLR-python

• https://github.com/tndoan/ISLR.jl

• The Matrix Calculus You Need For Deep Learning
• Deriving convolution from first principles

• http://www.cs.cmu.edu/~zkolter/course/linalg/linalg_notes.pdf
• http://joshua.smcvt.edu/linearalgebra/
• http://vmls-book.stanford.edu/
  • https://ses.library.usyd.edu.au/handle/2123/21370
• https://projects.iq.harvard.edu/stat110/home