Machine Learning Handbook:
Using R and Python
Dr. Karen Mazidi
Table of Contents
* Part 1 Introduction to Machine Learning and R
* 1 - The Craft of Machine Learning (pdf) (video)
* 2 - Learning Base R (pdf) (video) (video part 2)
* 3 - Data Visualization in Base R (pdf) (video) * 4 - The Craft 1: Planning to Learn
* Part 2 Linear Models
* 5 - Linear Regression
* 6 - Logistic Regression
* 7 - Naive Bayes
* 8 - The Craft 2: Predictive Modeling
* Part 3 Modern R
* 9 - The Tidyverse
* 10 - ggplot2
* 11 - The Craft 3: Data Wrangling in R
* Part 4 Searching for Similarity
* 12 - Instance-based Learning with kNN
* 13 - Clustering
* 14 - Decision Trees and Random Forests
* 15 - The Craft 4: Feature Engineering
* Part 5 Kernel Methods and Ensemble Methods
* 16 - Support Vector Machines
* 17 - Ensemble Methods and XGBoost
* 18 - The Craft 5: Learning Theory
* Part 6 Python for Machine Learning
* 19 - Python Basics
* 20 - Python Libraries for Machine Learning
* 21 - Python Machine Learning Examples
* 22 - The Craft 6: Data Wrangling with Python
* Part 7 Neural Networks
* 23 - Neural Networks
* 24 - Deep Learning
* 25 - The Craft 7: Never Stop Learning
* Part 8 Modeling the World
* 26 - Bayes Nets
* 27 - Markov Models
* 28 - Reinforcement Learning
* 29 - Never Stop Learning
