I use Python to re-produce the lab results and examples in each chapeter from the book Introduction to Statistical Learning with Application in R wittern by James, Witten, Hastie and Tibshirani.
It also includes the exercise solutions in Python3. This booked covers most of topics in machine learning.

• CHAPTER 2: Statistical Learning
• CHAPTER 3: Linear Regression
• CHAPTER 4: Classification

  • Logistic Regerssion
  • Linear Discrimnant Analysis
  • Quadratic Discrimnant Analysis
  • KNN

• CHAPTER 5: Resampling Methods

  • Cross Validation
  • Bootstrap

• CHAPTER 6: Linear Model Selection and Regularization

  • Best subset selection
  • Cross Valiation
  • Ridge/Lasso Regression
  • Principal Components Rregression
  • Partial Least Squares

• CHAPTER 7: Moving Beyond Linearity

  • Polynomial Regression and Step Function
  • Splines
  • GAMs

• CHAPTER 8: Tree-based Methods

  • Decision Trees
  • Bagging and Random Forests
  • Boosting

• CHAPTER 9: Support Vector Machines

  • Support Vector Classifier
  • Support Vectir Machine
  • SVM with Multiple Classes

• CHAPETER 10: Unsupervised Learning

  • PCA
  • Cluster Methods

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References:

• https://github.com/JWarmenhoven/ISLR-python
• http://www.science.smith.edu/~jcrouser/SDS293/labs/2016/lab8/Lab%208%20-%20Subset%20Selection%20in%20Python.pdf
• https://xavierbourretsicotte.github.io/subset_selection.html#Subset-selection-in-python
• https://jss367.github.io/Exploring-Decision-Trees-in-Python.html#decision-trees-for-classification
• contour plot reference http://www.adeveloperdiary.com/data-science/how-to-visualize-gradient-descent-using-contour-plot-in-python/