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Use intro text: An Introduction to Statistical Learning
With Python code also available here

Intro Topics:

1. Supervised Learning

   • Linear / Quadratic Discriminant Analysis

   • Naive Bayes

   • K-Nearest Neighbors

   • GLMs (ML vs Stats?)

     • Logistic Regression

     • Poisson Regression

   • Model Selection

   • Regularization

     • Ridge

     • Lasso

   • Non-Linear Models

     • Splines

     • Generalized Additive Models

   • Tree-Based Models

     • Decision Tree

     • Bagging, Random Forests, Boosting, BART

   • Support Vector Classifiers

     • Maximal Margin Classifier

     • Support Vector Classifier

     • Support Vector Machine

   • Survival Analysis

2. Resampling

   • Cross Validation

   • Bootstrap

3. Unsupervised Learning

   • Cluster Analysis

     • K-Means

     • Hierarchical Clustering

   • Principal Components

Advanced Topics:

1. NLP

   • Tokenization

   • Bag of Words

   • Embeddings

   • POS tagging

2. Neural Networks

   • Convolutional Neural Network

   • Recurrent Neural Network

3. Reinforcement Learning

To-Do:

• ensemble methods

• ADA boost

• XG boost