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It doesn’t require a genius to know that Machine Learning (ML) and Data Science are increasingly hot topics. Deep Learning is even touted as one of the most critical skills of today.
That being said, deep learning isn’t something that can be acquired easily. Machine Learning consists of working with a large volume of data. Data- that needs to be organized, analyzed, and stored. Later, algorithms are formed so that the machine can recognize the pattern and predict future behavior without human intervention.
Knowing the complexity of this field, it is no surprise that there is any number of books written on Machine Learning. These are targeted towards not only newbies but also professionals at intermediate or expert level. The authors try to include used cases, successful algorithms, and effective tricks and shortcuts.
Read on for the best Machine Learning books to read this year.
The 100 Page Machine Learning Book by Andriy Burkov
This book by Andriy Burkov summarizes various ML topics in an easy to comprehend manner. Burkov includes topics – both theory and practical –that are useful for practitioners. He doesn’t eliminate math equations, which is something most writers do in order to shorten their books.
One thing to keep in mind is that this book isn’t for beginners. Only individuals who have a basic understanding of Machine Learning will be able to comprehend the writing. This is because in many cases, Burkov depends on the knowledge of the readers and avoids simple definitions.
Fun Fact: This book originated from a LinkedIn challenge. In one of his posts, Burkov stated that ML literature doesn’t need to be around 500-1000 pages and that if he were to write a book, he would limit it to 100 pages. One of his followers challenged him to do so and surprisingly, he did!
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
While the book suggested before is one of the most compressed books about Machine Learning, Deep Learning is considered to be the most comprehensive book in the field. Also known as the Bible of Machine Learning, it’s written by three experienced authors, one of whom is considered the Godfather of the field.
This book isn’t for people who lack a solid algebraic foundation as it includes relevant topics in linear algebra, probability, numeric computation, etc. It comprises deep learning techniques used in the industry. Difficult topics like deep feedforward networks, regularization, and optimization algorithms are discussed in detail.
One distinctive factor that Deep Learning has is that it offers a research perspective too. Important headings like representation learning and auto-encoders are included.
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurelien Geron
One of the most-read books in the field of ML, Hands-On Machine Learning is the type of literature that teaches a concept and then educates the reader on how to apply concepts in real life.
This book is written with a perfect blend of theory and practicality. Throughout the book, readers will learn a range of techniques and tools such as classification models or dimensionality reduction. It helps in building intelligent systems on popular Python frameworks such as Scikit-Learn and TensorFlow.
Aurelien is known for her efficient communication and effective idea usage. She basks on that skill by implementing all the learnings the reader has inherited during the course of the book by using easy to implement examples. This ensures a practical understanding.
Machine Learning for Hackers by Drew Conway and John Myles
Despite what the title states, this isn’t a book for hackers. “Hackers” in this context means good software programmers. The book is targeted towards people who are interested in hands-on learning through case studies.
The main objective of Conway and Myles is to enable learning through algorithms in Machine Learning. Different chapters in the book focus on various topics of the field like optimization, prediction, or recommendation. Real-life cases are used and evaluated through the algorithms used in a particular situation.
This book stands out because it doesn’t initiate with heavy math-based explanations. It rather teaches you how to write simple Machine Learning algorithms in the R programming language.
Machine Learning (in Python and R) for Dummies by John Paul Muelle and Luca Massaron
Up until now, we’ve been recommending books that need a prerequisite knowledge of the basics of Machine Learning. However, this book is specifically written for beginners.
Written by two experienced data scientists, the book starts with basic concepts such as data analysis, data mining, and how to formulate common algorithms and goes up to learning how to code in R or Python. One interesting fact about this literature is that it also provides programming advice, including how to install R in Windows, Linux, macOS platforms.
Go ahead and pick one of these books on Machine Learning to get started!