Roman Orac

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Sep 21 · 5 min read

There are many great online resources to learn Data Science. Some free others paid. There are also expensive college programs dedicated to studying Artificial Intelligence. Which one should you choose?

Let me tell you a secret. Learning a new skill doesn’t have to be expensive. To learn a new skill in 2020, you only need time and dedication.

In this article, I’ve compiled a list of 7 free eBooks that will help you learn Data Science and Machine Learning. Keep on learning!

You only need time and dedication to learn a new skill in 2020

In case you would like to invest in yourself check the following courses:

Disclosure: Bear in mind that some of the links above are affiliate links and if you go through them to make a purchase I will earn a commission. Keep in mind that I link Udacity programs and my tutorials because of their quality and not because of the commission I receive from your purchases. The decision is yours, and whether or not you decide to buy something is completely up to you.

1. Deep Learning

Authors: Ian Goodfellow and Yoshua Bengio and Aaron Courville

Deep Learning book was originally released in 2016 as one of the first books dedicated to the field of Deep Learning. It was written by a team of standout researchers at the forefront of developments at the time and has remained a highly influential and regarded work in deep neural networks.

This is a bottom-up, theory-heavy treatise on Deep Learning. This is not a book full of code and corresponding comments, or a surface-level hand-wavy overview of neural networks. This is an in-depth mathematics-based explanation of the field.

2. Dive into Deep Learning

Authors: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola

Dive Into Deep Learning is an interactive Deep Learning book with code, math, and discussions. It provides NumPy/MXNet, PyTorch, and TensorFlow implementations. The authors are Amazon employees who use Amazon’s MXNet library to teach Deep Learning. The book is being regularly updated so be sure you’re reading the latest version.

Zachary Lipton puts it nicely:

What makes Dive into Deep Learning (D2K) unique is that we went so far with the idea of learning by doing that the entire book itself consists of runnable code. We tried to combine the best aspects of a textbook (clarity and math) with the best aspects of hands-on tutorials (practical skills, reference code, implementation tricks, and intuition). Each chapter section teaches a single key idea through multiple modalities, interweaving prose, math, and a self-contained implementation that can easily be grabbed and modified to give your projects a running start. We think this approach is essential for teaching deep learning because so much of the core knowledge in deep learning is derived from experimentation (vs. first principles).

3. Machine Learning Yearning

Author: Andrew Ng

This book was written by Andrew Ng, a professor at Stanford University and a pioneer in online education. He also co-founded Coursera and .

Machine Learning Yearning book focuses on teaching how to make ML algorithms work (not on teaching ML algorithms). It prioritizes the most promising directions for an AI project.

This book is a gem of useful information that will help you solve problems in practice, like diagnosing errors in ML systems, how to apply end-to-end learning, transfer learning, and multi-task learning, etc.

4. Interpretable Machine Learning

Subtitle: A Guide for Making Black Box Models Explainable
Author: Christoph Molnar

This book uses a “Pay what you want pricing strategy” so it is technically not free.

Interpretable Machine Learning focuses on ML models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks.

Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable. It details how to select and apply the best interpretation methods for a machine learning project.

5. Bayesian Methods for Hackers

Author: Cameron Davidson

Bayesian Methods for Hackers is not technically a Machine Learning book as it focuses on an important field of Data Science called Bayesian inference.

Bayesian Methods for Hackers is designed as an introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. It is aimed at enthusiast with a less mathematical background or one who is not interested in mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.

This book is also a great resource to learn PyMC, the probabilistic programming language in Python.

6. Python Data Science Handbook

Author: Jake VanderPlas

Python Data Science Handbook is aimed at junior Data Scientists. It shows how to use the most important tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and many others. This book is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.

7. An Introduction to Statistical Learning

Subtitle: with Applications in R
Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

An Introduction to Statistical Learning provides an introduction to statistical learning methods. It is aimed at upper-level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real-life settings and should be a valuable resource for a practicing data scientist.

Before you go

I am building an online business focused on Data Science. I tweet about how I’m doing it. Follow me there to join me on my journey.