DEV Community

Cover image for 8 Must-Read Machine Learning Books for All (level) Learners!
Tutort Academy
Tutort Academy

Posted on

8 Must-Read Machine Learning Books for All (level) Learners!

Machine learning is no longer just a buzzword; it's a pivotal aspect of modern technology that drives innovations across various industries.

Whether you're an aspiring data scientist or a software developer looking to delve into machine learning, finding the right learning resources is crucial.

However, with the plethora of options available, it can be overwhelming to choose the best ones, especially when considering affordability.

Here, we've curated a list of 8 affordable machine learning books that will pave the way for your machine learning expedition without breaking the bank.

Why Books to Learn Machine Learning?

Books serve as invaluable resources for immersing oneself in the fundamental concepts, terminology, and trends in machine learning.

They are a gateway to understanding whether a career in machine learning aligns with your aspirations, equipping you with the necessary skills, and staying abreast of the latest advancements in the field.

Additionally, they provide insights from experts, fostering a holistic understanding of the subject.

Affordable Machine Learning Books For Absolute Beginners

"1. Machine Learning for Absolute Beginners" by Oliver Theobald

  • Ideal for those with zero prior knowledge of coding, math, or statistics.

  • Provides a basic introduction to machine learning, accompanied by visuals, explanations, and code examples.

"2. The Hundred-Page Machine Learning Book" by Andriy Burkov

  • Offers a brief yet comprehensive overview of machine learning concepts.

  • Perfect for beginners aiming to grasp the fundamental principles of machine learning.

For Beginners with Python Experience

"3. Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas C. Müller and Sarah Guido

  • Tailored for individuals with Python proficiency entering the data science and machine learning field.

  • Provides a practical guide to implementing machine learning techniques using Python.

"4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurélien Géron

  • An excellent resource for practitioners seeking to apply machine learning to real-world problems.

  • Offers hands-on guidance on understanding and implementing ML concepts using popular frameworks.

For Programmers without Theoretical Knowledge

"5. Machine Learning for Hackers" by Drew Conway and John Myles White

  • Tailored for programmers with coding experience but limited exposure to the mathematical and statistical aspects of machine learning.

  • Uses practical case studies to ground abstract concepts in real-world applications.

"6. AI and Machine Learning For Coders: A Programmer's Guide to Artificial Intelligence" by Laurence Moroney

  • Provides coders with a comprehensive introduction to the practical aspects of implementing AI and machine learning.

  • A valuable resource for developers aiming to delve into the implementation of machine learning techniques.

For More In-depth Theory

"7. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies" by John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy

  • Offers practical applications and case studies alongside the theory behind machine learning.

  • Provides a comprehensive collection of algorithms and models for applying machine learning, particularly suited for those who develop on and with the internet.

"8. Data Mining: Practical Machine Learning Tools and Techniques" by Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal

  • A valuable resource for individuals seeking practical machine learning tools and techniques.
  • Offers insights into the application of data mining in real-world scenarios, making it an essential read for mastering machine learning.

By delving into these eight comprehensive machine learning books, individuals at various proficiency levels can deepen their understanding of machine learning, acquire practical skills, and stay abreast of the latest trends in this dynamic field.

Whether you're just starting your journey or aiming to enhance your expertise, these resources are designed to foster a holistic understanding of machine learning and its applications.

Top comments (0)