Artificial Intelligence (AI) and Machine Learning (ML) technologies have become key innovation accelerators for organizations looking for that extra edge. And with machine learning skills being in high demand, theres a surge in interest in this field. Machine Learning books are a great starting point for enthusiasts who want to transition to these in-demand roles. In this article we list down top machine learning books to get you started on ML journey.
The increased usage of machine learning in enterprises has driven up the need for skilled professionals. Machine learning models serve up Netflix recommendations, Facebooks News Feed leverages machine learning to drum up personalized content, and Twitter utilizes machine learning to rank tweets and boost engagements. Infact, anything that dishes out personalized feeds is driven by machine learning. The evolving field has given rise to new job roles such as machine learning engineers and data scientists.
According to SlashData, 45% of developers want to learn or improveOpens a new window their machine learning skills, and informative books and case studies serve us a good starting point for enhancing knowledge. We list down 15 books on machine learning that will help beginners, intermediate users, and advanced machine learning researchers broaden their understanding about the concept and learn from practical case studies.
Table of contents:
Five Best Books on Machine Learning for Beginners
- Machine Learning (in Python and R) For Dummies by John Paul Mueller and Luca Massaron
- Machine Learning for Absolute Beginners: A Plain English Introduction (Second Edition) by Oliver Theobald
- Machine Learning for Beginners by Scott Chesterton
- Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurelien Geron
- Python Data Analytics: With Pandas, NumPy, and Matplotlib by Fabio Nelli
Five Best Books on Machine learning for Intermediate Users
- Artificial Intelligence and Machine Learning for Business by Scott Chesterton
- Machine Learning with R (Expert techniques for predictive modelling) by Brett Lantz
- The Algorithmic Leader by Mike Walsh
- An Introduction to Statistical Learning
- Applied Predictive Modeling by Max Kuhn
Five Best Books on Machine Learning for Advanced Users
- Probabilistic Graphical Models: Principles and Techniques
- Advanced Deep Learning with Keras by Rowel Atienza
- Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
Five Best Books on Machine Learning for Beginners
Machine Learning (in Python and R) For Dummies by John Paul Mueller and Luca Massaron
The book offers advice on installing R on Windows, Linux and macOS platforms, creating matrices, interacting with data frames, working with vectors, performing basic statistical tasks, operating on probabilities, carrying out cross-validation, processing and leveraging data, working with linear models, and the idea behind different algorithms.
Who should read the book: The book is aimed at beginners – whether you need to learn how to code in R using RStudio, or code in Python using Anaconda, the book gives a lowdown on PythonOpens a new window and R. Authored by two experienced data scientists, the book is a handy guide for key concepts on data analysis, data mining and gives a lowdown on how to leverage common algorithms.
Machine Learning for Absolute Beginners: A Plain English Introduction (Second Edition) by Oliver Theobald
In this book, Oliver Theobald introduces enthusiasts with no prior coding experience to the practical components and statistical concepts in machine learning. Core algorithms in the book are accompanied by plain-English explanations and visual examples and readers are also taught about concepts such as Cross Validation, Ensemble Modeling, Grid Search, Feature Engineering, and One-hot Encoding.
Who should read the book: While the book is aimed at beginners, with no coding experience, it is also a handy guide for machine learning researchers and engineers. The book will also help sharpen knowledge about Regression Analysis, how to create trend lines, data scrubbing techniques and using Decision Trees to decode classification. The book also covers clustering techniques, largely used to build machine learning models for price predictions using Python, as well as artificial neural networks.
Machine Learning for Beginners: By Scott Chesterton
A veteran of over half a dozen books on machine learning, Scott Chesterton brings together the basic aspects of machine learning in this book, such as popular machine learning frameworks being used, machine learning algorithms, evaluation systems, data mining, and other common applications of machine learning. The book features commentaries on machine learning software such as TensorFlow, Reptilian, Logstash, Elasticsearch, Installing Marvel, Bro, HDFS, HBASE, Syslog, SNMP, messaging layer and real-time processing layer.
Who should read the book: Essentially for beginners, the book covers key concepts such as data preparation, cleaning datasets, classification, testing, induction and deduction, inductive preference, overfitting and underfitting, and text data extraction. Beginners interested in machine learning will also be able to learn key algorithms such as Decision Tree, Apriori, DBSCAN, Knowledge Mapping, Linear Models, K-Nearest Neighbors, support vector machine (SVM), FP-Growth and the new wave in ML – neural networks and the popular convolutional neural network algorithms along with their practical applications.
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Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools and Techniques to Build Intelligent Systems by Aurelien Geron
One of the most-read books in Artificial Intelligence and machine learning space, this handy guide by Aurelein Geron is a must-read for data scientists and machine learning enthusiasts looking for practical examples on how to implement ML tools.
Who should read the book: The book is aimed at readers who have Python coding experience. The book introduces readers to a number of techniques, ranging from simple linear regression to deep neural networks, for building intelligent systems on popular Python frameworks such as Scikit-Learn, Keras and TensorFlow. Readers can also learn how to train models such as support vector machines, decision trees, random forests, and ensemble methods work. The major prerequisite before you buy this book is to have a background in Python.
Python Data Analytics: With Pandas, NumPy, and Matplotlib by Fabio Nelli
Those with a baseline understanding of Python will find this book by Fabio Nelli useful for applying to real-world problems. Developers will be able to pick up techniques on social media analysis, image analysis with OpenCV and deep learning libraries. The book offers multiple examples of how Python can be used for data processing, management, and information retrieval. It also gives a peek into popular deep learning libraries such as PyTorch, TensorFlow, Keras, and Theano.
Who should read the book: For young data scientists and developers with knowledge of Python, this book offers the chance to learn how to deploy Python tools and techniques and use libraries such as NumPy, Matplotlib, and scikit-learn for data analysis, data visualization, and scientific computing.
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Five Best Books on Machine Learning for Intermediate Users
Artificial Intelligence and Machine Learning for Business by Scott Chesterton
This book by Scott Chesterton is not a long read or may not contain advanced coding examples, but acts as a good theoretical resource on how to operationalize AI and ML projects, how ML tools and techniques can be best utilized to process big data, and how to visualize a predictive models analytical results. The book is aimed at intermediate-level users who are familiar with machine learning tools, frameworks, and techniques.
Who should read the book: This book will be most useful for machine learning engineers and analytics managers at organizations who are looking to develop new AI and ML projects to spur business growth or to build their enterprise strategy. Through this book, Chesterton introduces readers to machine learning projects and how they can be used to improve an organizations capabilities and competitiveness and how machine learning teams can prepare for new challenges when deploying machine learning at scale.
Machine Learning with R (Expert techniques for predictive modelling) by Brett Lantz
While the initial chapters of Brett Lantz book feature plenty of information for beginners on machine learning, understanding how machines learn, and conducting machine learning with R, the narrative soon graduates to more technical aspects with information on practical applications.
Lantz also offers useful tips like generalizing tabular data structures with tibble, speeding and simplifying data preparation with dplyr, reading and writing to external data files, working with online data services, parsing XML documents and JSON from Web APIs, managing very large datasets, using massive matrices with bigmemory, conducting parallel cloud computing, deploying optimized learning algorithms, and performing GPU computing.
Who should read the book: Data Engineers and analytics teams can refer to the book to learn about regression methods for forecasting numeric data, applying linear regression to predict medical expenses, and evaluating and improving model performance.
The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You by Mike Walsh
While not really a tutorial on machine learning algorithms, frameworks, or techniques, The Algorithmic Leader by well-known futuristic Mike Walsh is a handy guide for industry leaders, business heads, and IT decision-makers. The book contains 10 principles of attaining success in the Algorithmic Age and these lessons are drawn out from case studies and interviews with AI pioneers, experienced data scientists, and top business leaders.
Who should read the book: Business leaders and industry veterans can use the book to understand the evolution and future of concepts like decentralization, digital disruption, probabilistic thinking, ethics, and machine learning and learn how to use these concepts for purposes such as problem-solving and decision making. It can also be used as a handy guide for laying down roadmaps for next-gen technologies in organizations.
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An Introduction to Statistical Learning with Applications in R (Springer Texts in Statistics)
This relatively-recent book by Gareth James, Daniela Witten, and Trevor Hastie is essential for intermediate users and newbie data scientists who want to refine their understanding of statistical modeling and prediction techniques and their respective applications in fields ranging from astrophysics to marketing to finance. The book presents statistical techniques and models in R, a language widely preferred by data science professionals, and contains color graphics and real-world illustrations to make it easier for readers to understand how various models are leveraged for practical uses.
Who should read the book: The book will help practitioners sharpen their understanding of supervised Learning algorithms such as tree-based methods, and Linear Regression as well as unsupervised learning techniques such as metrics, sampling, and clustering.
Applied Predictive Modeling by Max Kuhn
The winner of the 2014 Technometrics Ziegel Prize for Outstanding Book, Max Kuhns book is an essential part of graduate level predictive modeling courses, offering machine learning researchers and practitioners a way to gain an understanding about the overall predictive modeling process, including data preprocessing, data splitting, and model tuning.
Who should read the book: The book is aimed at researchers and practitioners and contains in-depth notes and real-life illustrations about modern regression and classification techniques. These processes are detailed out using extensive R code. Readers will also be able to learn about handling class imbalance, selecting predictors, and pinpointing causes of poor model performance to address practical concerns.
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Five Best Books on Machine Learning for Advanced Users
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) by Daphne Koller
Considering that uncertainty is an aspect all data scientists have to deal with when processing available data for real-world applications, this book by Daphne Koller explains in detail the art of using an array of probabilistic models that involve interpretable models to be constructed and then manipulated by reasoning algorithms.
Who should read the book: Aimed at data scientists, the book will help data science practitioners apply specific models which can learn from unstructured data and churn out actionable insights. The book also explains probabilistic graphical models (PGM) such as Bayesian networks and Markov networks, and conditional probability distributions.
Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more. By Rowel Atienza
Among the most popular books ever in the subject of programming algorithms, Rowel Atienza’s comprehensive guide offers lessons on evolving deep learning techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) that are churning out better AI results than ever before.
Who should read the book: The comprehensive guide allows advanced Machine Learning users to create cutting-edge AI algorithms using Keras as an open-source deep learning library. It gives readers a nuanced understanding about convolutional neural networks and recurrent neural networks that serve as the building blocks of advanced deep learning techniques. Readers will also be able to learn a lot about how to implement Deep Q-Learning and Policy Gradient Methods and the difference between Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs.
Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Dubbed as the only comprehensive book on the subject by well-known machine learning academicians Ian Goodfellow, Yoshua Bengio and Aaron Courville, the book offers advanced machine learning scientists and developers a lowdown on widely-used deep learning techniques such as deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.
Who should read the book: Aimed at data scientists, the book can help data scientists and ML practitioners sharpen their understanding of topics like linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning are also covered for the benefit of readers.
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Pattern Recognition and Machine Learning by Christopher M. Bishop
A best-seller and an industry favorite, this book by Christopher Bishop gives researchers, practitioners, and PhD students a rare introduction to pattern recognition through the Bayesian viewpoint. Readers are introduced to graphical models to describe probability distributions (an approach hardly any other author has covered) as well as to approximate inference algorithms that help generate approximate answers in situations where exact answers are not possible to generate.
Who should read the book: The book has been written for people pursuing advanced courses in machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics and will give readers an in-depth understanding of several approximate inference algorithms such as variational Bayes and expectation propagation. It also contains exercises that cover all difficulty levels to help readers test their understanding of pattern recognition.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
This hotseller by Kevin P. Murphy is a treasure trove of information on recent developments in AI such as conditional random fields, L1 regularization, and deep learning and their applications in varied fields like biology, text processing, computer vision, and robotics.
Who should read the book: The book also delves into concepts such as probability, optimization, and linear algebra to give readers an understanding of the underlying mathematics that powers the development of new ML tools and techniques. According to the author, the book offers a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book serves as a handy guide to practitioners and business leaders on the latest developments in the field.
Learn more: Top 10 Python Libraries for Machine Learning
Closing Thoughts
Machine Learning books provide a good starting point to enthusiasts and practitioners gain a deep understanding of the domain, up theoretical knowledge and lend practical tricks and insights which can be applied in day-to-day work. With AI/ML gaining traction in enterprises, comprehensive machine learning books, can potentially help reduce the ever-widening skills gap in data science professions in the future.
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