The Best Machine Learning Books
From a data scientist to a Machine Learning engineer, it’s essential that professionals have access to top-of-the-line Machine Learning books to gain knowledge and acquire untapped skills within their related fields.
Machine Learning is the future of the modern workforce. In fact, the World Economic Forum predicts that Artificial Intelligence, which includes Machine Learning, will create 12 million new jobs across 26 countries by 2025. If you want to get ahead of the competition, now’s the best time to crack open a book and start learning.
At Taylor & Francis, we publish Machine Learning books for all levels of study, from beginners to graduate students to experienced practitioners. Read on below to discover our top recommended books for Machine Learning at any level, beginning with fundamental concepts and ending with practical Machine Learning tool books.
Introductory material for beginners: What is Machine Learning?
As previously mentioned, Machine Learning (ML) is a branch of Artificial Intelligence (AI) and Data Science. It uses software applications, such as data and algorithms, to imitate how humans learn to more accurately predict new output values. Due to this specific type of programming, Machine Learning gradually improves in accuracy over time. Essentially the machine will learn by itself.
The process of Machine Learning uses historical observations or data for pattern recognition and then adjusts decisions based on those examples. This theoretically removes the need for human intervention or assistance.
The best Machine Learning books for beginners
A First Course in Machine Learning by Simon Rogers and Mark Girolami provides an essential, accessible introduction to Machine Learning for absolute beginners. With detailed explanations of the statistical learning methods and foundational concepts, this text is an integral part of any student’s introduction to Machine Learning. This book also features updated examples of code utilizing Python and R.
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In addition, Machine Learning: An Algorithmic Perspective, Second Edition helps computer science students without a strong statistical background build the foundational knowledge needed to pursue a deeper understanding of Machine Learning and provides better use of naming conventions in Python. This textbook is not only suitable for introductory courses, but also as a stepping stone for more advanced courses.
Find Out MoreWhy is Machine Learning important?
Machine Learning is an integral component of the increasingly important field of Data Science. It not only gives organizations an analytical view of customer behavior trends and operational patterns but also supports the creation and development of new products and services. For most industries, Machine Learning has become a clear way to stand out from the competition and optimize future decision-making and key insights.
Big Data grows larger each day and the market demand for data scientists and other professionals who understand Machine Learning correspondingly increases. If you’re looking to upskill and differentiate yourself from the pack, we’ve compiled the best books for understanding Machine Learning concepts.
Machine Learning principles and techniques
The ultimate goal of Machine Learning is to enable computers and robots to learn, in other words, modify or adapt to become more accurate, without manual programming by humans. Simple in concept, but very complicated in practice. So where should you begin?
A great place to start is with the Machine Learning book, Machine Learning and its Applications. This text explores the principles and techniques of common Machine Learning, including Bayesian models, graphical models, supporting a vector machine, decision tree induction, regression analysis, and recurrent and convolutional neural networks. In this book, you can learn or relearn the essential fundamentals of Machine Learning and its various applications in the modern world.
For example, Machine Learning and its Applications delves into the three main classifications of Machine Learning: supervised, unsupervised and semi-supervised learning.
Supervised learning
Supervised learning refers to a Machine Learning model that is applied when labeled datasets are present. The computer then uses the labeled data to train and test each ML algorithm. This technique allows companies to solve various real-world problems at scale, such as discerning between “spam” or “legitimate” email. The machine uses the labeled data to apply its knowledge to future unlabeled data and make its own decisions.
Additional methods used in supervised learning include linear regression, logistic regression, deep artificial neural networks, random forests, support vector machines (SVM) and more.
However, it’s important to note that labeling data is often a time-consuming and costly manual process for many companies.
Unsupervised learning
Unsupervised learning occurs when there is no labeled data present or available. The most popular unsupervised approach is called clustering.
Clustering refers to the process of understanding data by “finding the underlying structure of data.” Essentially, the computer will “cluster” groups of data together based on some similar measure. Machine Learning and its Applications gives an example of this where a company groups online users into customer groups based on similar purchasing behavior and demographics. The similar measures recognized by Machine Learning are purchasing habits and the age group. The company can then create an optimized marketing campaign to target specific groups.
Clustering refers to the process of understanding data by “finding the underlying structure of data.” Essentially, the computer will “cluster” groups of data together based on some similar measure. Machine Learning and its Applications gives an example of this where a company groups online users into customer groups based on similar purchasing behavior and demographics. The similar measures recognized by Machine Learning are purchasing habits and the age group. The company can then create an optimized marketing campaign to target specific groups.
Clustering Machine Learning algorithms usually include either k-means clustering, hierarchical clustering or principal component analysis (PCA). All of which are thoroughly explored in Machine Learning and its Applications.
Since unsupervised learning can’t rely on labeled data, evaluating a cluster can be extremely challenging. There is no “ground truth” with which the clustering task can be compared with.
Semi-supervised learning
Semi-supervised learning offers a combination of supervised and unsupervised learning. It’s used when a small amount of labeled data and a large amount of unlabeled data is available to the computer. In this case, Machine Learning uses that smaller set of labeled data to “train” and create a supervised Machine Learning algorithm to apply to a portion of the unlabeled data. The trained model that gives the best results is then combined with the labeled data sets to continue training the model. Each time, the model theoretically improves in accuracy and results.
This can help companies who don’t have enough time or money to label all of their vast amounts of data. Instead, they can label a portion and let the Machine Learning take over from there. Yet, this process does allow for human bias on the model, which should be noted.
Conversely, semi-supervised learning can also increase the accuracy of the model and even reveal new labels that weren’t previously identified.
Practical approaches to Machine Learning
Once you understand the basics, it’s similarly important to understand what practical approaches can be used with Machine Learning. Machine Learning: Algorithms and Applications digs into the numerous ways that Machine Learning has been applied in the past and is beginning to build toward in the future.
This Machine Learning book begins with the history of Machine Learning, as well as Artificial Intelligence as the larger category that houses Machine Learning. “Machine Learning” was a term created by Arthur Samuel in 1959 while he was working at IBM. He dictated that Machine Learning is a field of study “that gives computers the ability to learn without being explicitly programmed.” The authors then highlight Alan Turing’s first inquiry of whether a machine can think in 1955 — which is the start of Artificial Intelligence history and, consequently, Machine Learning.
Meanwhile, within a further subset of Machine Learning, there is Deep Learning, which aims to train a machine to learn based on how our own brains learn. For more information on Deep Learning techniques, consider Deep Learning in Practice.
Above is a graphic to better understand these three categories and their relationship with each other. Artificial Intelligence mirrors the intellectual abilities and behavioral patterns of humans. Machine Learning is the process through which a machine learns from data without the benefit of a detailed set of rules. Deep Learning is the method of achieving machine learning that is modeled on the human neural network.
Data Mining vs. Machine Learning
In Machine Learning: Algorithms and Applications, the authors note that there’s no clear distinction between Data Mining (DM) and Machine Learning in most literature. They define Data Mining as the “process of discovering patterns in data.”
Some publications highlight that there is a difference: Data Mining extracts data patterns and finds relationships between them, meanwhile, Machine Learning extracts data patterns and makes predictions. In other words, the difference is in the aim of Data Mining and Machine Learning. For future clarity, the book treats Machine Learning as a subarea of Data Mining where “the rules are learned automatically.”
Data Mining steps
There are four main steps to Data Mining:
- Data collection: Define data sources, extract data and store data.
- Data pre-processing: Data cleansing and data deduplication.
- Data analysis: Train models, evaluate models and predictive analytics.
- Data post-processing: Data visualization, reporting and trend analysis.
Data Mining is usually a repetitive process that goes through many iterations until the best (most accurate) results are achieved.
What is text mining in Machine Learning?
Text mining is “a knowledge-intensive process in which a user interacts with a collection of documents by using analytic tools in order to identify and explore interesting patterns.” This process is typically applied in marketing, competitive intelligence, health care, banking, manufacturing, natural sciences, security and many more domains.
If you’re interested in discovering more about Data Mining, a great book to explore is Text Mining with Machine Learning: Principles and Techniques, which dives into text mining, a subset of Data Mining. It explains several text mining tasks that are most commonly used in Machine Learning:
- Categorization of documents: Assigning a document to one or more predefined categories.
- Clustering: Grouping documents according to their similarity.
- Summarization: Finding the most important parts in one or more documents and creating a text that is significantly shorter than the original.
- Information retrieval: Retrieving documents that match a query representing information needed from a large collection of documents.
- Extracting the meaning of documents or their parts: Identifying hidden topics, analyzing sentiment, opinion or emotions.
- Information extraction: Extracting structured information like entities, events or relations from unstructured texts.
- Association mining: Finding associations between fundamental concepts or terms in texts.
- Trend analysis: Looking at how concepts contained in documents change in time.
- Machine translation: Converting a text written in one language to a text in another language.
For a concise and accessible introduction to text analytics or text mining, Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.
Find Out MoreSyntactic vs. semantics
The authors of this book emphasize that computers are only able to analyze the syntactic aspect of texts. Basically, they can recognize how words are arranged in documents, based on our language rules, such as grammar, when language processing.
Meanwhile, humans are able to understand the semantic aspect of texts. This is the meaning of a word or group of words in a context.
Thankfully, computers don’t have to have the full understanding of a text to solve practical problems since syntax and semantics are often close or the same. For example, if two separate texts use the same words and syntactic structures, they’re likely to carry the same semantic meaning. The computer would classify them in the same class of documents, which would most likely be correct.
