Machine Learning has grown to become very popular in the last few years and months. Industry analysts anticipate that Machine Learning, and broadly Artificial Intelligence, will be as impactful to humanity as the internet or the CPU.
If you want to learn Machine Learning, you are in the right place. This article is a guide on the best machine learning books for graduates.
What is Machine Learning?
Machine learning refers to the development and use of algorithms that enable machines to learn how to perform tasks instead of explicitly programming them to perform the said tasks.
Machine Learning is a field contained within Artificial Intelligence. Artificial Intelligence is more broadly concerned with developing intelligent behavior in computers. Machine Learning focuses on just one part of AI, learning.
How Is Machine Learning Being Used?
Computers have always been superior to humans on a scale. A computer can accurately do large amounts of work in a short time. However, computers were limited to performing only the tasks that humans understood well enough to write the code to instruct the computer. In other words, we were the bottleneck in what could be done by computers.
With Machine Learning, computers are no longer limited to what humans can express. This allows them to perform tasks that we previously found impossible or tedious to tell them how to do, such as:
When learning, books carry the advantage of providing a much deeper dive than all the other learning resources. Books go through an extensive writing process where they are written, and sentences are re-written for clarity.
The result is well-written prose that expresses ideas in close to the best way possible. My personal greatest reason for preferring text-based resources is how easy it is to reference and revisit some of the concepts. This is harder in video-based resources such as tutorials and courses. So, let’s explore the best books for learning machine learning.
The Hundred-Page Machine Learning Book
The Hundred-Page Machine Learning Book is exactly that, a book that teaches you machine learning in 100 pages. Because of the 100-page constraint, the book only gives you an overview of the subject without getting too much into the weeds.
From this book, you will receive an introduction to machine learning, but the author assumes no prior coding experience. Instead, explanations are given in plain English and graphical aids to make it easier to understand.
You will still learn to code, though, and the book includes some free, downloadable code exercises and supplementary video tutorials. However, this book alone will not make you a Machine Learning expert. You will still need to learn further with other resources.
This book is probably the most comprehensive you will find on Deep Learning. It was also written by a team of experts, including Ian Goodfellow, a research scientist who developed Generative Adversarial Networks.
It teaches you the mathematical concepts you will need to understand deep learning, including Linear Algebra, Probability Theory, Information Theory, and Numerical Computation.
The book covers the different types of networks used in Deep Learning, including Deep Feedforward Networks, Convolutional Neural Networks, and Optimisation Networks. Further, it was endorsed by Elon Musk as the only comprehensive book on the subject.
An Introduction to Statistical Learning
An Introduction to Statistical Learning provides an overview of the field of statistical learning. Statistical Learning is a subset of Machine Learning that includes learning methods such as linear regressions, classification, and support vector machines, among others.
All these techniques are covered in the book. To solidify the concepts covered, the book uses real-world examples. It focuses on implementing the concepts learned in R, a popular programming language used in machine learning that is used for statistical computing.
The book was written by Trevor Hastie, Robert Tibshirami, Daniela Witten, and Gartehm James, all of whom are professors of Statistics. Despite its strong grounding in statistics, the book should be fine for statisticians and non-statisticians.
Among other algorithms, it covers how recommendation systems, clustering, search engines, and optimization algorithms work. It includes concise code examples and exercises to help you practice.
The book was written by Toby Segaran, who also authored “Programming the Semantic Web” and “Beautiful Data”.
Fundamentals of Machine Learning for Predictive Data Analysis
This book introduces you to the core machine-learning approaches used in making predictions. Before covering practically the approaches to machine learning, the book gives an overview of the theoretical concepts you should know.
The book covers how to use machine learning to make price predictions, risk assessments, predict customer behavior, and classify documents.
It covers the four approaches to machine learning: information-based learning, error-based learning, similarity-based learning, and probability-based learning. It was written by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy.
Understand Machine Learning: From Theory to Algorithms
The book introduces machine learning and the algorithms that enable it. It provides a theoretical overview of machine learning fundamentals and how mathematics is derived.
It also shows how these fundamental principles are then translated into algorithms and code. These algorithms include stochastic gradient descent, neural networks, and structured output learning.
The book was written for graduates and advanced undergraduates by Shai Shalev-Shwartz and Shai Ben-David. A physical copy can be bought from Amazon, and a free online version is available here for download and non-commercial use.
Machine Learning for Hackers
Machine Learning for Hackers is a book written with experienced programmers in mind. It introduces you to machine learning in a hands-on and more practical manner. You will learn concepts from case studies instead of the mathematics-heavy approach taken by other books.
The book comprises chapters focusing on a specific area in machine learning, such as classification, prediction, optimization, and recommendation.
It focuses on implementing the models in the R programming language and includes exciting projects such as a spam email classifier, website page views predictor, and a single-letter decipher.
The book was written by Drew Conway and John Myles White, who both co-authored another book “Machine Learning for Email”.
Hands-on Machine Learning with R
Hands-On Machine Learning covers how to implement algorithms such as clustering algorithms, autoencoders, random forests, deep neural networks, and many others. The implementation is done using the R programming language and various packages within its ecosystem.
The book is not an R language tutorial itself. Therefore, readers should already be familiar with the language before using the book. A physical version of the book can be bought from Amazon, and an online version is available for free here.
Python Machine Learning
This book on Python Machine Learning introduces machine learning and how to implement it in Python. It begins by covering the basic and most fundamental libraries used in machine learning, such as NumPy for numerical computation and Pandas for dealing with tabular data.
It then introduces libraries such as scikit-learn, which is used to build machine learning models. The book also covers visualizing data using Matplotlib. It explains algorithms such as regression, clustering, and classification. It also covers how to deploy models.
Overall, this book is a comprehensive introduction to machine learning so you can begin implementing your own models and incorporating them into your applications. The book was written by Weng Meng Lee, the founder of Developer Learning Solutions.
Interpretable Machine Learning with Python
Interpretable Machine Learning with Python is a comprehensive guide to machine learning that gives an overview of machine learning models and how to mitigate prediction risks and enhance interpretability through practical examples and step-by-step code implementations.
By covering interpretability fundamentals, different model types, interpretation methods, and tuning techniques, the book equips readers with knowledge of interpretation and skills to improve machine learning models effectively. The book was written by Serg Masís, a climate and agronomic data scientist.
This list of books is obviously not exhaustive, but these are some of the best books to use to learn machine learning as a graduate. While most AI is implemented with code, you do not always have to write the code. There are lots of No Code AI tools to make it easier to develop.
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