Top 10 Must-Read Machine Learning Books for Tech Fanatics
To combat the thought of where to start, machine learning geeks can begin reading relative books.
“Today a reader, tomorrow a leader,” said the American journalist Margaret Fuller. People in tech can’t escape from this statement. Even though we say, technologies like artificial intelligence (AI) and machine learning (ML) are learned through practice and experience, reading a well-written full coverage book also serves the purpose. Machine learning has bestowed humanity the power to run tasks in an automated manner. The technology consists of working with a large volume of data that needs to be organised, analysed, and stored. Later, algorithms are formed so that the machine can recognise the pattern and predict future behaviour without human intervention. It is the growth and projections that have seen ML undoubtedly become the most in-demand technology of the modern era. Understanding the concept of machine learning starts from getting a grip on its fundamentals. It has so many fields, research topics and business use cases which are vast and void. To combat the thought of where to start, machine learning geeks can begin reading relative books. Henceforth, GlobalTech Outlook brings you a list of must-read machine learning books.
Author: Andriy Burkov
The Hundred-Page Machine Learning Book by Andriy Burkov is written to understand ML in an easy-to-comprehend manner. The book gives you an overlook on topics including the anatomy of a learning algorithm, fundamental algorithms, neural networks and deep learning, other forms of learning, and supervised and unsupervised learning, all just in hundred pages.
Author: Ian Goodfellow, Yoshua Bengio, Aaron Courville
The deep learning book is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The comprehensive book provides both general knowledge and the mathematical footing you need to get started within your own ML work.
Author: Oliver Theobald
Machine Learning For Absolute Beginners is a book for anybody who is entirely new to the ML world. Even if you don’t have programming knowledge or mathematical knowledge, you can still learn from the basics through this book. It has pretty visuals and graphs with an excellent explanation about algorithm, coding, Python, etc.
Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Introduction to Statistical Learning provides a beginner’s view of statistical learning methods. The book is aimed for undergraduates, masters and PhD students in the non-mathematical sciences. It also contains a number of R labs with detailed explanations on how to implement the various methods in real-life settings.
Author: Aurélien Géron
Hands-On Machine Learning with Scikit-Learn and TensorFlow provides a hands-on approach to learning by doing and covers many beginners and advanced techniques. The book is also loaded with tips and tricks for hacking machine learning. You can also learn techniques starting with simple linear regression and processing to deep neural networks.
Author: Drew Conway, John Myles White
Machine Learning for Hackers is meant for an experienced programmer interested in crunching data. In the title, ‘hackers’ refers to adroit mathematicians. As most of the book is based on data analysis in R, it is an excellent option for those with a good knowledge of R.
Author: Stuart J. Russell, Peter Norvig
Artificial Intelligence: A Modern Approach provides a thorough introduction to the machine learning field and gives an overview of several key research topics, walking the readers through how intelligent agents reach decisions and explaining neural networks in depth.
Author: John Paul Mueller, Luca Massaron
Machine Learning for Dummies aims to get readers familiar with basic concepts and theories of machine learning and how it applies to the real world. It presents the programming languages and tools integral to machine learning and illustrates how to turn seemingly-esoteric machine learning into something practical.
Author: Christopher M. Bishop
Pattern Recognition and Machine Learning present detailed practice exercises for offering a comprehensive introduction to statistical pattern recognition techniques. The book leverages graphical models in a unique way of describing probability distributions.
Author: Andreas C. Muller, Sarah Guido
Introduction to Machine Learning with Python comes with hands-on examples that help you learn the steps necessary to understand and create successful machine learning applications with Python and the scikit-learn library.