7 Free Books Every Data Scientist Should Read

Self-education is perhaps one of the most difficult paths and processes for an adult. With so many distractions around, it’s hard to bring yourself to follow through (especially if the motivation isn’t obvious). But self-education as evolution is an integral part of the life of any professional or anyone who wants to become one. In this case, books can become the same shot that kills two birds with one stone, you both grow as a specialist and do not “fall out of life.” The author of the material has selected 7 free e-books to help you learn Data Science and ML.

1. Deep Learning

By Ian Goodfellow, Joshua Bengio and Aaron Courville.
Deep learning first published in 2016. It was one of the first books dedicated to deep learning. The book was written by a team of distinguished researchers who were at the forefront of development at the time. This work in the field of neural networks remains influential and respected. The presented work is a theoretical treatise on deep learning, from basic concepts to modern ideas, such as complex generative networks and the application of machine learning in business and beyond. This book is a detailed, math-based explanation of the field of science. If you want to gain a broad basic knowledge of the most advanced elements of this field, this book is for you.

2. Dive Into Deep Learning

By Aston Zhang, Zach K. Lipton, Mu Lee, Alex J. Small
Dive Into Deep Learning Is an interactive deep learning book with code, math and commentary. It shows implementations in NumPy, MXNet, PyTorch, and TensorFlow. The authors are Amazon employees who use the Amazon MXNet library to teach deep learning. The book is updated regularly, so make sure you read the latest revision.

Zachary Lipton on the book:

What makes Dive into Deep Learning (D2K) unique? We’ve come so far with the idea of ​​learning by doing that the entire book is made up of executable code. We tried to combine the best aspects of the textbook (clarity and math) with the best aspects of how-to guides (acquired skills, reference code, implementation tricks, and an intuitive approach). Each section in the chapter teaches one key idea through multiple modalities: text, math, and code that can be easily understood and modified to get a project started quickly. We believe this approach is essential in teaching deep learning. Much of the core knowledge in deep learning comes from experimentation rather than basic principles.

3. Machine Learning Yearning

Author: Andrew Eun.
This book was written by Andrew Ng, a Stanford University professor and pioneer of online education. Andrew is one of the founders of Coursera and deeplearning.ai
Machine Learning Yearning teaches you how to make machine learning algorithms work, but not the algorithms themselves. It identifies the most promising areas for the AI ​​project. This book is a gem to help you solve practical problems like diagnosing errors in machine learning systems. She will teach you how to apply end-to-end learning, transfer learning, multitasking learning and more.

4. Interpretable Machine Learning

Author: Christoph Molnar.
This book is technically not free. It is sold on a pay-what-you-want basis.
Interpretable Machine Learning focuses on machine learning models for tabular data (also called relational or structured data) and pays less attention to computer vision and natural language processing tasks. This book is recommended for machine learning experts, data scientists, statisticians, and anyone interested in interpreting machine learning models. It details how to select and apply the best machine learning interpretation techniques in your project.

5. Bayesian Methods for Hackers

Posted by Cameron Davidson.
Bayesian Methods for Hackers The book focuses on an important area of ​​data science called bayesian inference… Bayesian Methods for Hackers is designed as an introduction to Bayesian inference from an understanding first, and then computation and mathematics. The book is aimed at enthusiasts who do not have a serious mathematical background, but who practice Bayesian methods. For such people, this text should be interesting enough. This book is also a great resource for learning PyMC, a probabilistic Python programming language.

6. Python Data Science Handbook

Posted by Jake Vanderplace.
Python Data Science Handbook targeted at young data scientists. It shows you how to work with the most important tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and many more. This book is ideal for solving day-to-day tasks such as cleaning, manipulating, and transforming data, as well as building machine learning models.

7. An Introduction to Statistical Learning

By Gareth James, Daniela Witten, Trevor Hasti, and Robert Tibshirani.
An Introduction to Statistical Learning is an introduction to statistical learning methods. The book is intended for senior students, masters and postgraduates of non-mathematical sciences. It contains a number of labs in R with detailed explanations of how to implement various methods in real-world environments. This text should be a valuable resource for the data practitioner.


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