This is a friendly handbook which is useful for familiarizing yourself with the basics of Machine Learning. I am preparing this handbook as part of "The Bit Series". Read more about our manifesto here. Also, feel free to submit pull requests as there might be plenty of grammatical errors, spelling errors, and even inconsistencies in the content. I will try to update the book as frequently as possible. I am also keeping a log of my continuous updates here. It is tough doing this alone, so I am definitely crediting anyone for their contributions.
👉 Table of Content
- Chapter 1 - Models and Cost Function
- Chapter 2 - Parameter Learning
- Chapter 3 - Multivariate Linear Regression
- Chapter 4 - Computing parameters analytically
- Chapter 5 - Regularization
- Chapter 6 - Neural Networks
- Draft stage
👉 TODO List
- Add examples through images.
- Work on readability using boldface, italics, tables, etc.