5 Best Machine Learning Books for ML Beginners
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Reading books is important because they books you endless knowledge.
If you plan to become a Data Scientist or Machine Learning Engineer, reading machine learning books will help you gain the knowledge and skills required in this field.
In a simple definition, machine learning is known as the subset of AI that has the ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise.
Email spam and malware filtering, product recommendations, image recognition, automatic language translation — these are all examples of real-life machine learning applications.
The Future of Jobs Report 2020 predicts that artificial intelligence (of which machine learning is a subset) will create 12 million new jobs across 26 countries by 2025.
There are many books available in the market to learn machine learning. In this article, I have compiled the 5 best machine learning books that will help you start building your career in this field. These books are in no particular rank or order.
So let’s get started!
1.The Hundred-Page Machine Learning Book
Author: Andriy Burkov
The author summarizes the complex topics in machine learning from various angles in some 100 pages. You will find the book provides enough details to get a comfortable level of understanding of machine learning concepts.
The book also comes in handy when brainstorming at the beginning of a machine learning project, you will learn how to answer the question of whether a given technical or business problem is “machine learnable” and if yes what technique you should use to try to solve the problem.
This machine learning book has been endorsed by the Director of Research at Google, Peter Norvig. I recommend every ML beginner should get their hands on this book.
This book covers the following topics.
- What is machine learning
- Notation and Definition
- Fundamental Algorithms
- Anatomy of a Learning Algorithm
- Basic Practice
- Neural Networks and Deep Learning
- Problems and Solutions
- Advanced Practice
- Unsupervised Learning
- Other Forms of Learning
Author: Aurélien Géron
This machine learning book gives you the concepts, tools, and intuition you need to implement programs capable of learning from data. Each chapter of the book has numerous exercises to help you apply what you have learned and this is a good approach to start gaining experience in machine learning.
You can find the code examples available online as Jupyter notebooks at https://github.com/ageron/handson-ml2.
Most of the examples, exercises and simple machine learning projects in this book are implemented by using popular python frameworks such as Scikit-learn, TensorFlow and Keras.
In this book, you will learn a large number of techniques, from the simplest and most commonly used machine learning algorithms (such as Linear Regression) to some of the deep learning techniques that regularly win ML competitions.
This book is organized into two parts. Part I focuses on The Fundamentals of Machine Learning and Part II focuses on Neural Networks and Deep Learning. To read this book, you must have some Python programming experience and that you are familiar with Python’s main scientific libraries such as NumPy, pandas, and Matplotlib.
This book covers the following topics:
- The Machine Learning landscape
- End-to-End Machine Learning Project
- Classification
- Training Models
- Support Vector Machines
- Decision Trees
- Ensemble Learning and Random Forests
- Dimensionality Reduction
- Unsupervised Learning Techniques
- More topics in Neural Networks and Deep Learning
Author: Oliver Theobald
This is a good book to pick if you are looking for an introduction into the world of machine learning. The author focuses on teaching the high-level fundamentals of machine learning as well as the mathematical and statistical underpinnings of designing machine learning models.
To read this book, you don’t need to have prior experience in machine learning.
This book covers the following topics:
- what is machine learning
- machine-learning categories
- Regression Analysis
- Clustering
- Bias and Variance
- Artificial Neural Networks
- Decision Trees
- Ensemble Modeling
- Build a model in Python
- Model Optimization
Authors: Steven Bird, Ewan Klein and Edward Loper
If you are interested in natural language processing (NLP) and becoming an NLP Practitioner, I recommend you read this book. The book provides a highly accessible practical introduction to the field of natural language processing and it contains hundreds of examples and exercises to help you gain more experience.
The book is based on the Python programming language together with an open-source library called the Natural Language Toolkit (NLTK). If you haven’t learned the Python programming language, don’t worry this book has a few sub-topics to teach you the Python programming language.
In this book, you will learn how to access well-annotated datasets for analyzing and dealing with unstructured data using python, linguistic structure in text, and other NLP-oriented aspects.
This book covers the following topics:
- Language Processing and Python
- Accessing Text Corpora and Lexical Resources
- Processing Raw Text
- Writing Structured Programs
- Categorizing and Tagging Words
- learning to Classify Text
- Extracting Information from Text
- Analyzing Sentence Structure
- Building Feature-Based Grammars
- Analyzing the Meaning of Sentences
- Managing Linguistics Data
Authors: Andreas C. Müller and Sarah Guido
This is a very practical book for readers who are comfortable programming in Python, and want to learn machine learning in a practical way. In this book, you will learn various practical ways to build your own robust machine learning solutions
The main focus is to teach you how to build machine learning applications using open-source Python libraries such as Scikit-learn, Pandas, Numpy and Matplotlib in a way that is easy to follow and very hands-on.
You will also learn when a certain business problem demands or can be improved with machine learning and the whole workflow of a machine learning project( data-preparation, pre-processing, training, evaluating and implementing into production).
You can find code examples and notebooks available for download at https://github.com/amueller/introduction_to_ml_with_python.
This book covers the following topics:
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning and Preprocessing
- Representing Data and Engineering Features
- Model Evaluation and Improvement
- Algorithm Chains and Pipelines
- Working with Text Data
Final Thoughts on 5 Best Machine Learning Books for ML beginners
Books remain one of the best ways to gain new knowledge. This collection of machine learning books mentioned above will help you to learn a lot about machine learning.
But it is important to remember to put your knowledge into practice by working on different small machine learning projects and participating in machine learning competitions such as in Kaggle and zindi. By doing so, you will continue to learn more and get more experience working in machine learning.
If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post!
You can also find me on Twitter @Davis_McDavid.
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