Machine learning is increasingly integrated into our lives. This is the field of artificial intelligence (AI) that powers chatbots, customizes the shows Netflix recommends, and determines your TikTok feed.
As companies begin to understand the value of machine learning, the demand for skilled machine learning engineers and data scientists is also increasing. According to the World Economic Forum, jobs for AI and machine learning specialists are among the fastest growing jobs in the world, with net growth expected to be more than 80% between 2025 and 2030. [1].
Reading books is a great way to understand key concepts, terminology, and trends in machine learning. We’ve curated a list of machine learning books for beginners, from general overviews to focused areas like statistics, deep learning, and predictive analytics. By adding these books to your reading list, you’ll be able to:
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Decide if a career in machine learning is right for you
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Learn the skills you need to be a machine learning engineer or data scientist
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Gain knowledge to help you search and prepare for job interviews
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Stay on top of the latest trends in machine learning and artificial intelligence
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Hear from knowledgeable experts in the field
Bookmark this page now so you can revisit it during your machine learning journey.
If you’re ready to build your machine learning skills today, consider enrolling in Stanford University and DeepLearning.AI’s Machine Learning Specialization. In just two months, you’ll have the opportunity to build machine learning models, build and train supervised models, and more. Once completed, you will have earned a career certificate that you can add to your resume.
9 books to learn machine learning for beginners
There are many great books on machine learning and artificial intelligence, but these books are especially helpful for beginners who are new to the field. Most of these provide an overview or introduction to machine learning through the lens of a specific focus area, such as case studies, algorithms, statistics, or for those who already know Python.
1. 100 page machine learning book Written by Andriy Burkov
The best introduction to machine learning
At just over 100 pages, this book provides a thorough introduction to machine learning in an easy-to-understand style for AI systems. Data professionals can use it to expand their machine learning knowledge. After reading this book, you will be prepared to talk about basic concepts in an interview. This book combines both theory and practice, explaining important approaches such as classical linear regression and logistic regression using diagrams, models, and algorithms written in Python.
2. Machine learning for absolute beginners Written by Oliver Theobald
Perfect for complete beginners
As the title suggests, this book provides a basic introduction to machine learning for beginners with no prior knowledge of coding, mathematics, or statistics. Theobald’s book is step-by-step, written in plain language, and includes visuals and explanations along with each machine learning algorithm.
If you’re completely new to machine learning and data science, this book is for you.
3. Machine learning for hackers Written by Drew Conway and John Miles White
Perfect for programmers (prefer practical case studies)
The authors use the term “hacker” to refer to programmers who hack code for a specific purpose or project, rather than individuals who gain unauthorized access to people’s data. This book is perfect for people who have experience with programming and coding, but are less familiar with the math and statistical aspects of machine learning.
This book uses case studies that demonstrate practical applications of machine learning algorithms to help place mathematical theory in the real world. Examples such as how to create recommendations for Twitter followers are based on abstract concepts.
Did you know?
AI has enabled machines to write books with minimal human input. Deep learning using large-scale language models (LLMs) like ChatGPT produces human-like text.
The AI book project is based on long short-term memory (LSTM) algorithms, which enable feedback connections and processing of entire data sequences. This concept may seem creepy, but it pushes the boundaries of what’s possible. Books written by AI can be found at Booksby.ai.
4. Practical Machine Learning with Scikit-Learn, Keras, and TensorFlow Written by Aurelien Geron
Perfect for people who know Python
If you already have experience with the Python programming language, this book provides further guidance in understanding the concepts and tools needed to develop intelligent systems. chapters of Machine learning practice Includes exercises to apply what you’ve learned.
Use this book as a resource to develop project-based technical skills that will help you land a machine learning job.
5. deep learning Written by Ian Goodfellow, Joshua Bengio, and Aaron Courville
best books on deep learning
This book provides a beginner’s introduction for those interested in the deep learning aspects of machine learning. deep learning Explore key concepts and topics in deep learning, including linear algebra, probability, and information theory.
bonus: The book comes with lectures with slides on the website and exercises on GitHub.
Develop your deep learning skills with this specialization from DeepLearning.AI.
6. Overview of statistical learning Written by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Ideal for statistical approaches
This book is an excellent tool for those who already have some statistical knowledge. Understand statistical learning and uncover the process of managing and understanding complex datasets. It covers important concepts such as linear regression, tree-based models, and resampling techniques, and includes many tutorials (using R) for applying these techniques to machine learning.
7. Collective intelligence programming Written by Toby Segalan
The perfect guide for practical application
Dig deeper into machine learning with this book and you’ll learn how to create algorithms for specific projects. This is a practical guide that explains how to access data from websites and other applications and customize the programs that collect and use that data. Eventually, they will be able to create algorithms that detect patterns in data, such as how to predict product recommendations on social media or match singles with dating profiles.
8. Fundamentals of machine learning for predictive data analysis Written by John D. Kelleher, Brian Mac Namey, Aoife Darcy
Ideal for analytical approaches
This is another book that provides practical applications and case studies along with the theory behind machine learning. This book is written for people who develop on or using the Internet. It takes the guesswork out of predictive data analysis and provides a comprehensive collection of algorithms and models for applying machine learning.
read more: Data analysis: definitions, usage, examples, and more
To improve your data analysis skills, IBM’s Generative AI Specialization for Data Analysts:
9. Machine learning for humans Written by Vishal Maini and Samar Sabri
Great for free resources
The last one is an e-book that you can download for free [2]. This is a clear, easy-to-read guide for machine learning beginners, complete with code, math, and real-world examples for context. In five chapters, you’ll learn why machine learning is important, then understand supervised and unsupervised learning, neural networks and deep learning, and reinforcement learning. As a bonus, we’ve included a list of resources for further learning.
Machine learning in literature
“Why” book Judea Pearl and Dana Mackenzie propose the value of causality in data and how it can contribute to social good (such as the relationship between carbon emissions and global warming). This concept of causality forms the basis of both human and artificial intelligence.
If fiction is faster for you, look no further than Isaac Asimov’s classic. me, robotimagines how humans and robots struggle to survive together. Other science fiction writers, such as Ted Chiang, explore our relationship with AI technology in stories such as: Software object lifecycle.
Improve your machine learning skills today with free resources
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