Machine learning has emerged as a very popular technology useful in predicting outcomes from data using machine learning algorithms. Machine learning combines computing with neuroscience and consists of the autonomous learning of a machine using algorithms. We know that this may sound very simple, but the truth is that it is a fairly complex process, in which machines learn through various patterns of behavior that replicate human cognitive abilities. If you are a beginner looking for which machine learning books to read, this article is for you!
What is Machine Learning?
Before approaching any of the books to learn Machine Learning that we are going to recommend, it is important that you have a minimum knowledge of this system. Machine Learning is essentially responsible for everything that has to do with Artificial Intelligence and the development of systems or programs that work practically without human intervention.
The fundamentals of Machine Learning are the development of algorithms that execute certain types of tasks, but not only that but every time it receives data, the program or machine can learn from experience and adapt to any alteration that occurs.
To learn about machine learning, read our blog – What is machine learning?
Popular Machine Learning Books
If you are interested in knowing what Machine Learning is, it is important that you know what the basic bibliography is, to begin with. There is an infinite list of books to learn Machine Learning, but it is humanly impossible to read all the available bibliography on the subject, so we have prepared a small selection of books for you to start with.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
This book gives an overview of machine learning and how to use it in practice. With this book, you are going to learn machine learning very well. Each chapter the philosophy of each machine learning technique, how it works, examples in Python, and for which cases each technique is recommended. The book covers both classic machine learning and deep learning.
Accompanying the book, the author has created a repository on GitHub where he explains in detail each example in Python. In addition, it provides the solutions to the proposed exercises.
Aurélien Géron, the author, has done a great job of explaining complex concepts very clearly. The book is available in English, French, and Portuguese.
The Hundred-Page Machine Learning Book by Andriy Burkov
The Hundred-Page Machine Learning Book is one of the bestsellers by Andriy Burkov, a seasoned AI & ML expert with around two decades of industry experience.
Hundred-Page Machine Learning Book is a practical guide to machine learning. Anyone willing to learn the concepts of ML without having much prior knowledge of ML can read this book and get started. Starting from basics and then moving on to the advanced ML topics, this book can help apply ML in your day-to-day professional work. The book also covers information like Q&A, code snippets, further reading, tools, and other relevant resources.
Hundred-Page Machine Learning Book is available at flexible prices and formats. You have the option to choose from – Kindle, hardcover, paperback, EPUB, and PDF.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Data manipulation is a fundamental task in the machine learning process and consumes the most time. Pandas is a Python library that, together with NumPy, makes data manipulation and exploratory analysis very easy. It will also serve us on many occasions to extract characteristics (features), clean data, combine them, sample them, etc.
Wes McKinney, the author of the book, is the creator and “Benevolent Dictator for Life” (BDFL) of the open-source Pandas’ package for data analysis in the Python programming language. Therefore, no secret escapes him; he knows how to convey everything that pandas can do. The author has also made available a repository on GitHub where he develops the book examples.
The book Python for Data Analysis will help you improve your knowledge of pandas quickly so you’ll know what’s possible, why, and what works the best, which data processing methodology should be chosen over another (e.g., processing speed, memory used, etc.). For specific questions, the Pandas documentation is also excellent.
Tip – Buy the latest edition.
Deep Learning with Python
Keras is a Python library that lets you build deep learning models easily. Keras uses TensorFlow internally but offers a very easy-to-use programming interface for common deep learning problems such as convolutional neural networks, recurring neural networks, etc.
François Chollet, the author of Deep Learning with Python, is also the creator of the Keras deep-learning library and a main contributor to the TensorFlow machine learning framework. So what better person to learn it from?
In addition, François has prepared a repository on GitHub where he develops the contents of the book. In short, it is a highly recommended book if you want to learn how to use deep learning.
Building Machine Learning Powered Applications: Going from Idea to Product
With this book, you will learn what is necessary to design and develop applications based on Machine Learning. This book is intended to help data scientists and software engineers navigate through Machine Learning tools.
Understanding Machine Learning: From Theory to Algorithms
Good books for learning Machine Learning should not only focus on developing practical examples on how to create algorithms for Machine Learning and Artificial Intelligence. In this book, you can learn about the theoretical basics of Machine Learning and about how the construction of algorithms is the foundation of the latest technological developments.
Must Read – Top 10 Machine Learning Algorithms for Beginners
Machine Learning Applications Using Python – Cases studies from Healthcare, Retail, and Finance
This is one of the books to learn Machine Learning and should not be missed if you wish to learn the basics of machine learning using Python and its libraries. From the study of concrete examples in areas as diverse as health, sales, and finance, you will understand the importance of Machine Learning and what impact it has led in developing more efficient models.
Fundamentals of Machine Learning for Predictive Data Analytics
If you have already passed the beginner stage in Machine Learning, it is time to ascend a step and it is best to do it with this book. As its name indicates its premise ”Fundamentals”, this book covers all the principles of machine learning and seeks to deepen the theory, through the application of practices, examples, and case studies.
In addition, it covers different approaches to Machine Learning, as well as concepts of machine learning with algorithms and models, accompanied by practical examples that reflect the concepts.
Now that you know which are the best books to learn machine learning, you are one step closer to developing your goals in computer science. Technology advances at accelerated steps, the learning of algorithms and machines is increasing, and the future is definitely going to be shaped by developments in Artificial Intelligence.
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