In this book, you will learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have a strong aptitude for mathematics and some necessary programming skills, this book will help you get into the core of data science in a satisfying way. There are many books available online, which gives you the basic idea of the implementation of statistical models by using libraries. But after all, these libraries are made from scratch. So if you want to learn data science from scratch and enhance your knowledge in this domain, then this book will definitely help you achieve your goal.

TinyML is an excellent book authored by Google engineers Pete Warden and (former) Daniel Situnayake, which shows us how to create mini-machine-learning projects on embedded devices. To enjoy most of this book, you will need a bit about machine learning and software development. However, the authors make it very straightforward and assume that readers do not have a background in either ML or software engineering. We at Towards AI are very excited about this book because it breaks the gap and showcases how to build tiny ML applications on tiny devices, helping those with fewer resources get access to the fun that it's to work with machine learning, and to get you, even more, excited the authors have released a free intro to the first six chapters of the book and a companion to video-tutorials on how to get the most out of the book.

This book gives us a lot of real-life examples of how statistical concepts apply in the real world. The tone of the book is witty and conversational. The author of this book does not go deep into the theories, but instead, he uses pretty compelling examples to help you understand even some of the complex statistical concepts. This book starts with fundamental concepts of statistics like a normal distribution, central limit theorem, and goes on to complex real-world problems and correlating data analysis and machine learning. All in all, if you are new to data science, this book will make you laugh while understanding statistical concepts.

This book mostly focuses on applying machine learning techniques to solve natural language processing (NLP) problems. All those interested in Natural Language Processing (NLP) with Python should refer to this book. The writing of this book is straightforward and presented in a very tidy fashion. Moreover, the book presents code examples in Python in a precise way. The topics covered in this book are -- extracting features from plain text, analyzing linguistic structure, accessing popular NLP datasets, NLTK, and many more.