This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. Each chapter in this book corresponds to a single machine learning method or group of methods. In other words, each chapter focuses on a single tool within the ML toolbox.
In my experience, the best way to become comfortable with these methods is to see them derived from scratch, both in theory and in code. The purpose of this book is to provide those derivations. Each chapter is broken into three sections. The concept sections introduce the methods conceptually and derive their results mathematically. The construction sections show how to construct the methods from scratch using Python. The implementation sections demonstrate how to apply the methods using packages in Python like scikit-learn, statsmodels, and tensorflow.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Machine Learning from Scratch for free.
- Title
- Machine Learning from Scratch
- Author(s)
- Daniel Friedman
- Published
- 2021-11-15
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 109
- Language
- English
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Introduction Table of Contents Conventions and Notation 1. Ordinary Linear Regression Concept Construction Implementation 2. Linear Regression Extensions Concept Construction Implementation 3. Discriminative Classifiers (Logistic Regression) Concept Construction Implementation 4. Generative Classifiers (Naive Bayes) Concept Construction Implementation 5. Decision Trees Concept Construction Implementation 6. Tree Ensemble Methods Concept Construction Implementation 7. Neural Networks Concept Construction Implementation Appendix Math Probability Common Methods Datasets