Table of contentsKey takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology
So, you’re looking for machine learning books, either to dive into the domain for the very first time, or to expand your knowledge, or even to brush up on your skills. This list has machine learning books for beginners, intermediates, and experts.
The best machine learning books for beginners
Author - Tom M. Mitchell
This one is a great book if you’re just diving into machine learning. It gives you a good overview of machine learning theorems and even has pseudocode summaries of the algorithms. It explains machine learning basics rather well and even has project-oriented homework assignments.
Tom M. Mitchell explains genetic algorithms, inductive logic programming, reinforcement learning, and other ML concepts and techniques rather well here. You’ll even be introduced to the primary approaches to machine learning.
Author - Ethem Alpaydin
This book dives into the basics of machine learning. It covers the evolution of ML as well as some important learning algorithms and explains how they could be applied. It even helps you understand machine learning algorithms for pattern recognition, artificial neural networks, reinforcement learning, data science. It also covers the ethical and legal implications of ML for data privacy and security.
Author – David Barber
This one is great for computer scientists who want to explore machine learning but don’t have a particularly strong base in calculus and linear algebra. It also comes with extra online resources as well as a software package with demos and teaching materials that instructors can use. It covers approximate interference, dynamic models, the framework of graphical models, learning in probabilistic models, the naive Bayes algorithm, as well as probabilistic reasoning.
Authors – Shai Shalev-Shwartz and Shai Ben-David
Understanding Machine Learning explains the fundamental theories and algorithmic paradigms of machine learning and mathematical derivations. It explains the computational complexity of learning, helps you understand convexity and stability, and even breaks down neural networks, machine learning algorithms, the PAC-Bayes approach, as well as stochastic gradient descent, and structured output learning.
Author – Oliver Theobald
As the title suggests, you don’t need any experience in or understanding of machine learning to get started with this book. You don’t even need a background in coding or mathematics. It explains neural networks, clustering, cross-validation, regression analysis, data scrubbing techniques, ensemble modeling, and feature engineering in an extremely simple way and even offers visual examples along with ML algorithms.
Author – John Paul Mueller and Luca Massaron
In true For Dummies style, Machine Learning for Dummies seek to familiarize you with the basic ML concepts and theories. It concentrates on practical, real-world applications of machine learning. It makes use of Python and R code to show you how you can train machines to find patterns and analyze results. It also talks about how machine learning enables email filters, fraud detection, internet ads, web searches.
Author – Peter Harrington
This one is useful for undergrad students as well as working professionals. It explains machine learning techniques as well as their underlying concepts in a rather thorough manner. If you are a developer attempting to write your own programs for acquiring data to analyze it, this book can be quite a good guide for you. Along with the basics of machine learning, it also covers big data and MapReduce, FP-growth, K-means clustering, logistic regression, support vector machines, and tree-based regression.
Author – Andreas C. Müller & Sarah Guido
This one is great if you’re a data scientist with proficiency in Python and you want to learn machine learning. It shows you a lot of practical ways of building machine learning solutions of your own. It shows you how to create powerful machine learning applications with Python and Scikit-learn library. If you understand matplotlib and NumPy libraries well, it’ll be even easier for you to learn. Along with the fundamentals of machine learning, it also explains advanced methods for model evaluation and parameter tuning.
Author – Leonard Eddison
Along with the basics of machine learning, it also teaches you the basics of artificial intelligence and the fundamentals of Python programming. It even discusses the several branches of machine learning and their applications. The book even covers decision trees, deep neural networks, and logistic regression.
Authors – Ian Goodfellow, Yoshua Bengio and Aaron Courville
This one gives you an introduction to several topics on deep learning and even explains machine learning aspects that are related. It does a great job of explaining the fundamentals of deep learning and covering concepts like linear algebra, probability and information theory, numerical computation. It even covers techniques like optimization algorithms, convolutional networks, computer vision as well as research topics like Monte Carlo methods, and Partition Function.
Author – Aurélien Géron
This is one of the best-selling machine learning books for beginners. You need some understanding of Python programming to get started with this book. It explains machine learning libraries like Scikit-Learn, Keras, and TensorFlow 2 and how they can be used to build intelligent systems.
The best machine learning books for intermediates and experts
Authors – Drew Conway and John Myles White
This book is great if you’ve got experience in ML and want to use it to crunch numbers and analyze data. If you’ve got a decent understanding of R, you should totally go for it as it focuses on data analysis in R and even touches on employing advanced R for data wrangling.
Authors – Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal
This one gets into the technical details of machine learning, shows you how to use data mining techniques to find patterns in large data sets through methods belonging to the fields of database systems, machine learning, and statistics. It compares various data mining techniques and covers instance-based learning, linear models, statistical modeling, and predicting performance.
Author – Nishant Shukla
This book gives you a lot of practical coding experience, along with a great explanation of machine learning concepts. It delves into deep learning concepts so that you can be prepared for several types of machine learning tasks using the open-source TensorFlow library.
Author – Christopher M. Bishop
This is a brilliant resource if you want to understand and employ statistical techniques in machine learning and pattern recognition. You do need to have a good understanding of linear algebra and multivariate calculus if you want to get value from this book. It will be even easier for you to learn from this one if you have some experience with probability.
Authors – Max Kuhn, and Kjell Johnson
It focuses on data collection, manipulation, and transformation processes. It’s great if you want to analyze real problems faced by industries. Applied Predictive Modeling allows you to explore data preprocessing, splitting, model tuning, regression, classification, handling class imbalance, and selecting predictors.
Authors – Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Here there is more emphasis on the concepts than on the mathematics behind the concepts. It’s a must have for statisticians and data mining enthusiasts. It goes over supervised and unsupervised learning, support vector machines, classification trees, neural networks, boosting, ensemble methods, graphical models, spectral clustering, least angle regression, path algorithms, and much more.
Authors – Sebastian Raschka, and Vahid Mirjalili
Since you already have a pretty solid understanding of Python and machine learning, this one dives directly into how you can implement the concepts you’ve learned. It covers dimensionality reduction, ensemble learning, regression, and clustering analysis, neural networks, etc. and teaches you from real-world challenges that arise in the industry.
Authors – Daniel Jurafsky and James H. Martin
If you have even a decent understanding of machine learning, this one will be rather good for you. AI and ML professionals highly recommend this book for those who want to dive into NLP. This book focuses heavily on practical applications of speech and language processing.
Author – John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy
Predicitive analytics involves using an array of statistical techniques to analyze past and current events to make future predictions. To gain value from this book, you’ll need to understand the basics of predictive data analytics. The book covers error-based learning, information-based learning, probability-based learning, similarity-based learning, and even the techniques that you can use to evaluate prediction models.
Author – Toby Segaran
This book shows you how to create efficient machine learning algorithms to mine and gather data from applications, build programs to access data from websites, and infer the gathered data. It covers Bayesian filtering, collaborative filtering techniques, methods for detecting groups or patterns, non-negative matrix factorization, search engine algorithms and much more.
Author – Andriy Burkov
This book comes highly recommended by Director of Research at Google, Peter Norvig, and Head of Engineering at eBay, Sujeet Varakhedi, so you know you just have to read it. It can help you build and appreciate complex AI systems, and do much more. It even explains the anatomy of learning algorithms and lets you brush up on Neural networks and deep learning as well as fundamental algorithms.
There you have it - 22 machine learning books for every level. Whether you’re a machine learning beginner, you have a bit of experience, or you’re a bonafide machine learning expert, you could gain some value from these books.