AI & Python #27: Books I Read to Learn Data Science and Machine Learning

Books handpicked by me.

Photo by Daria Nepriakhina on Unsplash

I’ve made a list of 5 books I read some years ago.

I recommend reading them if you’ve been dabbling in data science, ML, or AI and would like to learn them from scratch. Also, the books are great for those with some previous knowledge of Python, statistics, or math and would like to know how that knowledge can be used in data science, ML, and AI.

#1 “Data Science from Scratch: First Principles with Python” by Joel Grus

Book cover image from Amazon

This book aims to help you develop the hacking skills necessary to get started doing data science. After reading the book, you’d be comfortable with the maths and statistics that are at the core of data science. Although there’s a chapter in this book called “A Crash Course in Python,” the book’s goal is not to teach you Python from scratch, so, in case you have never written code in Python, you should supplement this book with one of my recommended Python books for beginners.

The book is packed with topics that every data scientist should be familiar with in the fields of statistics (probability, hypothesis, and inference), linear algebra (vectors and matrices), and machine learning (theory and concepts behind machine learning models).

Keep in mind that this first book mostly gives an overview of the topics mentioned before. To dive deeper into these topics, consider reading the books listed below in no particular order.