/Machine_Learning_2nd_edition

Machine Learning with R and Python

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Machine Learning Handbook Using R and Python

author: Dr. Karen Mazidi

Materials to accompany the book, still in progress.

Table of Contents

Part One: Introduction to Machine Learning

  1. The Craft of Machine Learning
  2. Learning R
  3. Data Visualization in R
  4. The Craft 1: Planning to Learn

Part Two: Linear Models

  1. Linear Regression
  2. Logistic Regression
  3. Naive Bayes
  4. The Craft 2: Predictive Analytics

Part Three: Modern R

  1. The Tidyverse
  2. ggplot2
  3. The Craft 3: Data Wrangling

Part Four: Searching for Similarity

  1. Instance-based learning with kNN
  2. Clustering
  3. Decision Trees and Random Forests
  4. The Craft 4: Feature Engineering

Part Five: Kernel Methods and Ensemble Methods

  1. Support Vector Machines
  2. Ensemble Methods
  3. Semi-supervised Learning
  4. The Craft 5: Choosing Algorithms

Part Six: Neural Networks

  1. Neural Networks
  2. Deep Learning with Keras
  3. The Craft 6: Big(ish) Data

Part Seven: Modeling the World

  1. Bayes Nets
  2. Markov Models
  3. Reinforcement Learning
  4. The Craft 7: Learning Theory