Machine learning (ML) is a sub-field of artificial intelligence (AI) that enhances software applications’ accuracy without explicit programming changes. It uses algorithms to predict future outcomes based on historical data input. All machine learning books talk about four basic approaches to machine learning, categorized based on how an algorithm predicts. There is supervised, unsupervised, semi-supervised, and reinforcement learning.
- Supervised learning: In this, algorithms are applied with labeled training data and pre-defined variables to be assessed by the algorithm. Here, both the input and the output are specified.
- Unsupervised learning: In this approach, algorithms are trained on unlabeled data and try to find a meaningful connection. The data and the recommendations (or predictions) are predetermined.
- Semi-supervised learning: This approach is a combination of the above two types. Here, the training data may be labeled, but even then, the model is allowed to find connections on its own.
- Reinforcement learning: Reinforcement learning is used to train a machine to do a pre-defined multi-step process. The algorithms are programmed to perform a task with positive/negative cues during the process. However, the algorithm majorly decides on its own.
Many study materials are available if you wish to learn more about machine learning and algorithms. Choosing the correct reference can be tedious if you do not know what you are looking for. Here is a list of some top machine learning books and a brief description of what they offer to make your task easier.
Top Machine Learning Books
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow – 2nd Edition
If you are familiar with Python programming, this machine learning book is probably the best guiding material for understanding concepts. Written by Geron Aurelien, the book also explains the tools and frameworks required to build intelligent systems. Extra attention has been given to frameworks like TensorFlow, Keras, and Scikit-Learn. Each chapter is written in a reader-friendly manner and features exercises to apply the concepts learned in the previous chapters. You can refer to the book to hone your technical skills for projects and advance your machine learning career.
Link to the book: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Industries like medicine, finance, marketing, etc., house much information (potential data) that can be used to analyze specific trends and patterns. However, it is challenging to understand and analyze this data. This machine learning book, written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, discusses several tools and techniques that have surfaced to overcome the challenges of understanding data. It discusses data mining techniques, machine learning models, and bioinformatics. The book also discusses many ideas from a statistical perspective. Despite the statistical approach, the focus is maintained on the concepts, not the mathematical end.
- Machine Learning for Streaming Data with Python
Joos Korstanje’s machine learning book highlights the need for real-time data analysis due to evolving business and data streaming technologies. It focuses on adopting machine learning techniques to deal with data more efficiently. In the initial chapters, you will learn about the basic architecture of streaming data and real-time machine learning. The following few chapters mention a few state-of-the-art frameworks like River. In the following chapters, industrial use cases and challenges will be discussed. Once you finish the book, you will be confident in streaming data and working on your machine learning models.
Link to the book: Machine Learning for Streaming Data with Python
- The Big Book of Machine Learning Use Cases – Databricks eBook
This comprehensive guide on machine learning can give you a quick start in your career. As machine learning technologies are ever-evolving, finding more relevant real-life uses becomes challenging. With this book, you can directly jump to applying the mentioned use cases, code scripts, and notebooks. You will also learn about dynamic time warping, decision trees to detect financial frauds, and sales trends via MLflows. The book will also enable you to perform multivariate time series forecasting using RNNS (recurrent neural networks). Additionally, the ebook mentions several case studies from companies like Comcast, Nationwide, and Regeneron. With this ML book, you can instantly start using the Databricks Lakehouse Platform.
Link to the book: The Big Book of Machine Learning Use Cases
- Fundamentals of Machine Learning for Predictive Data Analysis
This book, written by John D. Kelleher, is one of the best machine learning books following an analytical approach. After getting familiarized with the basic concepts of machine learning, the next step is to learn about those concepts’ practical applications and real-world use cases. Knowing about the practical use of machine learning fundamentals will help you professionally, and referring to this book will help you become proficient in predictive data analytics as it provides a comprehensive collection of ML algorithms and models.
Link to the book: Fundamentals of Machine Learning for Predictive Data Analysis
Read More: Top AI Technology Trends to Dominate 2022
- Approaching (Almost) Any Machine Learning Problem
Written by Abhishek Thakur, Approaching (Almost) Any Machine Learning Problem talks about coding and practical applications. It is the perfect book for you if you are a machine learning practitioner and do not wish to delve into theoretical concepts in detail. The book is very practical and contains many coding scripts that form the backbone of machine learning algorithms. This book is an excellent problem-solving tool if you have basic knowledge about related concepts. It explains how to set up an environment, cross-validation, evaluation metrics, feature engineering, hyperparameter optimization, and many other processes. It can be referred to as a guide with readily applicable solutions.
Link to the book: Approaching (Almost) Any Machine Learning Problem
- AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence
Written by Laurence Moroney, based on his successful AI and ML courses, the book is an ideal place to start your transition from programming to becoming an AI and machine learning specialist. It is an introductory book that will offer a hands-on and code-based approach to enhancing your programming skills. The book also explains several machine learning scenarios like computer vision, sequence modeling, cloud computing, TensorFlow, and natural language processing (NLP). By the end of this book, you will be confident enough to build your own models using TensorFlow, code samples, and NLP-based tokenization and ultimately serve these models over the web via clouds.
Link to the book: AI and Machine Learning for Coders
- Pattern Recognition and Machine Learning
Written by Christopher M. Bishop, this machine learning book is an excellent reference for learning and using statistical techniques to recognize patterns in data and machine learning. If you are acquainted with linear algebra and multivariate calculus, you will be able to gain a lot from this book. It leverages graphic models by describing probability distributions and features detailed practice exercises.
Link to the book: Pattern Recognition and Machine Learning
- Applied Predictive Modeling
Applied Predictive Modeling is an introductory guide to predictive modeling and its application. Written by Dr. Kuhn and Dr. Johnson, the book will also be a treat for non-mathematical readers because it provides intuitive explanations with a problem-solving approach. The wide-ranging applications will help practitioners hone their expertise in working with accurate data. However, the book will be helpful if you have a basic understanding of statistical concepts like regression, correlation, etc. The majority of this book does not contain many complex computations, but you will need a mathematical background to understand some topics.
Link to the book: Applied Predictive Modeling
- Machine Learning Yearning
Machine Learning Yearning, written by Andrew Ng, provides expert guidance to ML practitioners in decision-making, data collection, debugging, etc. It introduces all necessary topics to understand machine learning and mentions real-world case studies. However, it cannot work as a guide to ML modeling as it does not contain any codes, but it will discuss all necessary background information. It is preferable for non-mathematical readers who do not want to delve into complex computations and suit those with prior experience with machine learning models but who need a more intuitive understanding.
Link to the book: Machine Learning Yearning