300+ Free AI, ML, Data Science Books
300+ free AI, ML, data science books
The year has just started and we are already thinking about the books that we are going to read by the end of 2021. Insane did some digging and has collected over 300 free AI, ML, data science, and programming books. If you are new to the field, there is everything needed to get you started. Already a few years of experience under your belt? No worries, some of these books will help you take things to the next level. You can find the three lists below:
To give you an idea of what to expect, we put together a quick selection of some of the available books:
Data Science Books
This handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. These 25 experts hail from a wide selection of background, disciplines, and industries.
As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. The authors examine practical ways for making ethical data standards part of your work every day.
Experienced SQL developers all over the world share their favourite SQL techniques and features.
Learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before.
Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. Through examples, you’ll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. Once done, you’ll have a strong foundation for a lifetime of data science learning and practice.
Today we are witnessing an increased use of data visualization in society. Across domains such as work, education and the news, various forms of graphs, charts and maps are used to explain, convince and tell stories. In an era in which more and more data are produced and circulated digitally, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to be of societal benefit. This book is a contribution to the multi-disciplined and multi-faceted conversation concerning the forms, uses and roles of data visualization in society. Do data visualizations do ‘good’ or ‘bad’? Do they promote understanding and engagement, or do they do ideological work, privileging certain views of the world over others? The contributions in the book engage with these core questions from a range of disciplinary perspectives.
AI and ML Books
Artificial Intelligence has the power to advance humankind more than fire and electricity. Everywhere. The authors believe it is of greatest importance that AI knowledge and technology is available, usable and affordable for all – not only the big and powerful. The book explains the fundamentals of AI, its potential benefits, and how businesses can make AI operational and create positive change.
The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
Machine Learning and Deep Learning are transforming numerous industries. Andrew Ng wrote this book to teach you ow to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/ test sets
- Set up an ML project to compare to and/or surpass human- level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle–Build, Preproduction, Deployment, Monitoring, and Governance–uncovering how robust MLOps processes can be infused throughout.
A thorough overview of the ongoing evolution in the application of AI within healthcare and radiology. Readers get a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging.
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the “how” transcends the creation. Likewise, an understanding of the “how” transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.
The goal of this book is to help you start your programming journey a year or two ahead of where we were when we started. There’s a lot of how-to, a splash of career advice, and a bit of pep talk. It’s a good read for Computer Science majors, dev bootcamp students, beginning devs on a self-learning path, or anyone who wants to figure out if programming is for them.
The author explains some simple constructs provided by Python, some essential tips, and some use cases he comes up with regularly in his Data Science work. Most of the book is of a practical nature and you will find it beaming with examples.
The popular C programming language is used for a huge range of applications, from the tiny microcontrollers used in toasters and watches up to complete operating systems. Even if you are an absolute beginner, this book will teach you all you need to know to – Create simple command-line C programs – Control flow with conditions and loops – Handle variables, strings, and files – Design graphical user interface applications in C – Handle user input with buttons and menus, etc.
PHP is a widely-used open source programming language. It is especially suited for web development. The unique thing about PHP is that it serves both beginners as well as experienced developers. It has a low barrier to entry so it is easy to get started with. At the same time, it provides advanced features offered in other programming languages.