Introduction
Chapter One: What is Machine Learning?
Subjects involved in machine learning
Chapter Two: Uses of Machine Learning
Density Estimation
Latent Variables
Reduction of Dimensionality
Visualization
Varieties of Machine Learning
Chapter Three: Common Machine Learning Terms
Classification
Regression
Decision Trees
Clustering
Support Vector Machines
Deep Learning
Neural Networks
Generative Model
Chapter Four: Supervised Machine Learning
Overview
Issues to consider in Supervised Learning
Other factors to consider
Chapter Five: Unsupervised Machine Learning
Chapter Six: Reinforcement Learning
Applications of Reinforcement Learning
Chapter Seven: Machine Learning Algorithms
K Nearest Neighbors
Naïve Bayes Estimation and Bayesian Networks
Regression Modeling
Support Vector Machine
Decision Trees
Genetic Algorithms
Conclusion
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