Machine learning: an applied mathematics introduction

Table of Contents:

1.K Nearest Neighbours
2.K Means Clustering
3.Naïve Bayes Classifier
4.Regression Methods
5.Support Vector Machines
6.Self-Organizing Maps
7.Decision Trees
8.Neural Networks
9.Reinforcement Learning

The book includes many real-world examples from a variety of fields including finance (volatility modelling), economics (interest rates, inflation and GDP), politics (classifying politicians according to their voting records, and using speeches to determine whether a politician is left or right wing), biology (recognising flower varieties, and using heights and weights of adults to determine gender), sociology (classifying locations according to crime statistics), and gambling (fruit machines and Blackjack).

https://pandaohana.com/the-book/