15 Completely Free Machine Learning and Deep Learning Books
Must read books if you are studying Data Science
There are a ton of books and courses available on the internet which can help you master python and data science. Previously I created a list of the 22 best resources to master python in this article.
However, if you are looking for books that can help you learn the detail and theory behind data science topics, the previous article was not covering those.
So, in this article, I will try to fill the gap. Fortunately, there are a lot of completely free eBooks available online which can cover the majority of the topics and concepts; we need to learn as Data Scientists.
Here is a list of my favorite data science books.
1. Think Stats
By Allen B. Downey
For python developers, Think Stats is a beginner friendly introduction to Statistics and Probability. You can either read this book online or download it as a PDF from the official greenteapress website.
You can follow the books provided coded examples to learn statistics concepts and practical skills to work with data. This makes learning a lot easier and digesting mathematical equations fun.
Key topics: Artificial Neural Networks
Reader level: Beginner.
Programming language: Python
Read this book online.
Download link: http://greenteapress.com/thinkstats2/thinkstats2.pdf
Code examples and solutions are available from this GitHub repository.
2. Dive into Deep Learning
By Aston Zhang, Zachary C. Lipton, Mi Li, and Alexander J. Smole
If you want to understand how deep learning models work and how you can start? Then Dive into Deep Learning is your answer. It takes you on a journey by starting with the foundation of machine learning; covering more complex parts of deep learning later on.
Key topics: Artificial Neural Networks
Reader level: Beginner.
Programming language: Python
Download link: https://www.intechopen.com/books/6187
Write better python code with the help of these python tips and tricks.
3. Advanced Applications for Artificial Neural Networks
By Adel El-Shahat
This book will help you understand the current capabilities of artificial neural networks and how they will grow in the future. What I like most about the books are case studies like high-resolution soil property ANN maps and hardware ANN maps for gait generation of multi-legged robots and many more.
Key topics: Artificial Neural Networks
Reader level: Beginner.
Programming language: Python
Download link: https://www.intechopen.com/books/6187
4. Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models
By Przemyslaw Biecek and Tomasz Burzykowski
As Data Scientists we help businesses to make the right decision with the help of data. To do that we need to learn the skill of explaining the analysis of a model. This book can help you to learn model analysis skills.
Key topics: Explanatory Model Analysis.
Reader level: Beginner to Advanced.
Programming language: Python and R
Download link: https://ema.drwhy.ai/
5. Bayesian Methods for Hackers: Probabilistic Programming for Bayesian Inference
By Cameron Davidson-Pilon
Bayesian inference is an essential part of many probabilistic machine-learning methods. If you are new to Bayesian inference and need a really good introduction then Bayesian Methods for Hackers is a great choice.
This book fills the gap by explaining the theoretical math equations and explaining them in practical application.
Key topics: Bayesian methods for Data Science.
Reader level: Beginner.
Programming language: Python
Download link: https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
6. Deep Learning for Coders with Fastai & PyTorch
By Jeremy Howard and Sylvain Gugger
This book is designed to start with your deep learning journey. Even if you already have little exposure to deep learning, this book can help. It covers the use of multiple layers of neural networks for human speech recognition to animal image classification. You can read this book while following the fast.ai deep learning course https://course.fast.ai/.
Key topics: Deep Learning with PyTorch and Fastai.
Reader level: Beginner to Advanced.
Programming language: Python
Download link: https://fastai.github.io/fastbook2e/
7. Deep Learning on Graphs
By Yao Ma and Jiliang Tang
Graphs can be used easily interpretable, explainable, and sample. This book focuses on the extensive deep-learning techniques needed to build Graph Neural Networks(GNNs). Also, it covers the application of GNNs in different areas such as Computer Vision, Natural Language Processing, Healthcare, and Data Mining.
Key topics: Graph Neural Networks(GNNs)
Reader level: Beginner.
Programming language: Python
Book official link: https://web.njit.edu/~ym329/dlg_book/
Download link: https://web.njit.edu/~ym329/dlg_book/dlg_book.pdf
8. Neural Network Design
By Martin T. Hagan, Howard B. Demuth, Mark Hudson Beale, and Orlando De Jesús
In this ebook, you will start with the fundamental of neural network architectures and learning rules and then further go into the perceptron learning rule. Features extensive coverage of training methods for both Feedforward networks including multilayer and radial basis networks. You will then get more theory vector spaces, linear transformation, and more.
Key topics: Neural Network Design
Reader level: Beginner
Programming language: Python
Download link: https://hagan.okstate.edu/nnd.html
9. Physics-based Deep Learning
By N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, and K. Um
This book is about the practical and comprehensive introduction to physical simulations in deep learning. You will learn also learn differentiable simulations, training algorithms tailored to physics problems as well as reinforcement learning and uncertainty modeling. Along with theory on topics including NNS, differentiable physics, reinforcement learning, and physical losses; you will find hands-on code examples using a Jupyter notebook.
Key topics: Physics-based simulation
Reader level: Advance
Programming language: Python
Download link: https://physicsbaseddeeplearning.org/intro.html
10. Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning
By Jean Gallier and Jocelyn Quaintance
This book will help you to understand how mathematics can be applied to Machine Learning. Finally, with the help of this book, you will able to explain different Data Science methods mathematically.
Key topics: Algebra, Topology, Differential Calculus
Reader level: Advance
Programming language: Python
Download link: https://www.cis.upenn.edu/~jean/gbooks/geomath.html
11. Natural Language Processing with Python
By Steven Bird, Ewan Klein, and Edward Loper
This book is a fantastic introduction to learning natural language processing with python. It’s a beginner-friendly introduction to NLP using the NLTK toolkit to mine, analyse, process, and classify text data.
Key topics: natural language processing and text mining.
Reader level: beginner.
Programming language: python
Book link: https://www.nltk.org/book/
12. Machine Learning Yearning
By Andrew Ng
This book was created based on Andrew Ng’s experience in leading the Google brain team. It draws on the practical steps and frameworks for successful machine learning projects. It also has really useful guidance on handling unbalanced data, splitting data, validation, and building complex machine-learning models.
Key topics: building successful machine learning systems.
Reader level: intermediate.
Programming language: None.
Downloading link: https://info.deeplearning.ai/machine-learning-yearning-book
13. An Introduction to Statistical Learning
By Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Every day we are increasing the data we are collecting each day. Statistical learning is a critical toolkit for anyone who wants to understand the data. This book can help anyone to learn key methods in statistical learning.
Key topics: machine learning.
Reader level: beginner.
Programming language: python.
Download Link: https://www.statlearning.com/
14. Linear Algebra
By Jim Hefferon
Linear Algebra by Jim Hefferon is the foundation of the mathematical field in machine learning. This book covers Linear Systems and Gauss’s method, vector spaces, linear maps, and matrices. It also contains a set of exercises to practice and test your learning.
Key topics: Linear algebra
Reader level: beginner.
Programming language: python.
Downloading link: https://hefferon.net/linearalgebra/
15. Forecasting: Principles and Practice
By Rob H Hyndman and George Athanasopoulos
In this book, you are going to learn about different methods used for forecasting. Including a lot of examples with real data to analysis and validity of fitted in different models such as linear and nonlinear, ARIMA models, and neural networks. In R it uses the tsibble and fable packages which allows us to integrate closely with tidyverse.
Key topics: forecasting.
Reader level: beginner to advanced.
Programming language: R.
Book link: https://otexts.com/fpp3/
I hope you found a book that can kickstart your career and improve your Data Science knowledge. However, more books can ease your learning but they are not completely free. You can check this comprehensive list of machine learning books collected on Github.
Thanks for reading! if you like this, it will boost me to work hard and feel free to give your feedback and suggestion through a comment to improve my work. 😊
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