Data Science (DS) and Machine Learning (ML) have become extremely popular in today’s time. Data science is basically the extraction of hidden information from raw data, while machine learning is the method by which we used to train machines to mimic human behavior, and learn from past experiences. As several businesses are producing more and more data, they need more data scientists and ML engineers in order to get hidden insights from that data. Overall, there is a huge demand for data scientists and ML engineers across industries.
It is always good to keep yourself updated by reading excellent books related to your domain. Here, we will discuss the top 10 free must-read books for machine learning and data science, which you should definitely read.
Python Data Science Handbook (By Jake VanderPlas)
This is one of the most popular free books for data science and machine learning. It introduces the core Python libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages for working with data in Python. The book makes an assumption that the reader has a basic understanding of the Python language.
Mathematics for Machine Learning (By Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong)
This book motivates people to learn essential mathematical concepts for machine learning. Here, you will not find any advanced machine learning techniques. Instead, this book aims to provide the necessary mathematical skills to understand advanced machine learning algorithms.
Neural Networks and Deep Learning (By Michael Nielsen)
Neural Network and Deep Learning is a popular free online book where you will study neural networks, a beautifully explained programming paradigm that enables a computer to learn from observational data and deep learning. It is a powerful set of techniques for learning in neural networks. This excellent book will help you in learning the core concepts of artificial neural networks and deep learning.
Think Bayes (By Allen B. Downey)
Think Bayes is an excellent free must-read book on Bayesian statistics using computational methods. The theme of this book, as well as other Think X series books, is that if you know programming, you can use that skill to learn other concepts. This book uses Python code instead of mathematics to explain Bayesian statistics and mathematical concepts.
Statistical Inference for Data Science (By Brian Caffo)
Statistical Inference for Data Science is another free must-read book for machine learning and data science, which is written as a companion book to the Statistical Inference Coursera class as part of the data science specialization. This book is for numerically and computationally literate people who want to apply these skills in machine learning and data science.
Understanding Machine Learning: From Theory to Algorithms (By Shai Shalev-Shwartz, Shai Ben-David)
This is another must-read book for understanding the theory and concepts behind machine learning algorithms. The book introduces machine learning and algorithmic paradigms in a principled manner. It also covers a wide array of central topics that are otherwise ignored by other books.
The Art of Data Science (By Roger D. Peng and Elizabeth Matsui)
The Art of Data Science is a must-read free book that focuses on the analysis and distillation of data. The book is a distillation of authors’ extensive experience in both managing data analysts and conducting their own data analyses. It is written in such a style that is suitable for both practitioners and managers.
The Data Science Handbook (By Carl Shan, Henry Wang, William Chen, and Max Song)
This amazing free book is a compilation of interviews with 25 top data scientists in which they share their vast career experience, insights, and advice. Although this book is not a technical guide to machine learning and data science, you will enjoy the stories of noted personalities and industry practitioners helping readers figure out their own plan of action.
Machine Learning Yearning (By Andrew Ng)
Andrew Ng is a famous personality in the artificial intelligence (AI) and machine learning (ML) domain. He states that the objective of this book is to “teach one how to make the numerous decisions needed with organizing a machine learning project.”
Generally, it takes years to learn AI and ML strategies, but this book will help you quickly gain these skills so that you can become better at building AI systems.
Natural Language Processing With Python (By Steven Bird, Ewan Klein, and Edward Loper)
This is another popular must-read book for machine learning and Natural Language Processing (NLP). The term “natural language” means a language that is used for day-to-day communication by humans, just like English or Hindi. This book takes NLP in a wide sense to cover any kind of computer manipulation of natural language with the help of Python together with an open-source library called the Natural Language Toolkit (NLTK).
Finally, we have talked about the top 10 free must-read books for machine learning and the data science domain. These are not the only books that we should consider for ML and data science. Instead, there are many other excellent free books that would teach you the core concepts of machine learning and data science.
Frequently Asked Questions (FAQs)
Q. What are data science and machine learning?
Ans: Data science is the process of exploring, cleaning, and analyzing the data and getting some hidden patterns from it. With the help of data science, businesses and organizations can improve their products or services, thereby improving the overall customer experience.
Machine learning is the subdomain of artificial intelligence in which we train machines to mimic human behaviour without explicitly being programmed for it.
Q. What is the future of data science and machine learning?
Ans: As more and more data is generated through individuals and organizations, the need for data scientists and ML engineers will also increase. There is already a shortage of skilled workforce in this domain. So, the future of data science and ML is promising.