Learning ML

Learning ML

A practical guide to understanding and applying machine learning algorithms in the quest to become a 🦄.

by @blairhudson

This book is a work in progress. Follow @Learning_ML for the latest updates! For detailed changes, see commits on GitHub.

Table of Contents


1. Introduction

2. Classification

3. Regression

  • Linear Regression
  • Support Vector Regression

4. Unsupervised

  • k-means
  • t-SNE
  • Apriori
  • PCA
  • LDA

5. Deep Learning

  • Keras and TensorFlow
  • Deep Neural Networks: Classification and Regression
  • DNN Problems and Architectures

6. Big Data

  • Spark MLLib

Appendix


learning.ml / This work is licensed under CC BY-NC-ND 3.0 AU.