machine learning has become more and more integrated into our lives. It’s the branch of artificial intelligence (AI) that powers chatbots, personalizes the shows Netflix recommends to you, and determines your TikTok feed. plays an important role in health care technology, where machines can diagnose treatment and even perform surgery.
As businesses begin to understand the value of machine learning, the demand for machine learning-trained engineers and data scientists is also growing. surprisingly, in 2019, machine learning engineer was ranked as the best job in the united states, based on a 344% job growth between 2015 and 2018 [1].
You are reading: Machine learning books for beginners
If you’re curious about machine learning, books are a wonderful way to immerse yourself in the key concepts, terminology, and trends. We’ve curated a list of machine learning books for beginners, from general overviews to those with focus areas such as statistics, deep learning, and predictive analytics. With these books on your reading list, you’ll:
-
determine if a career in machine learning is right for you
Find out what skills you’ll need as a machine learning engineer or data scientist
knowledge that can help you find and prepare for job interviews
stay on top of the latest trends in machine learning and artificial intelligence
bookmark this page now so you can revisit it throughout your machine learning journey.
books on machine learning for beginners.
There are many excellent books on machine learning and artificial intelligence, but these titles are especially useful for beginners who are just discovering the field. most of these offer an overview of machine learning or an introduction through the lens of a specific focus area, such as case studies and algorithms, statistics, or those who already know python.
1. andriy burkov’s hundred page machine learning books
best for machine learning
In just (just over) a hundred pages, this book provides a solid introduction to machine learning. it’s written in a way that makes AI systems very easy to understand so you’ll be prepared to talk about basics in an interview. The book combines theory and practice, illuminating significant approaches such as classical linear and logistic regression with illustrations, models, and algorithms written in Python.
Not for absolute beginners, but a great introduction to the topic and for data professionals looking to broaden their understanding of machine learning.
2. machine learning for absolute beginners by oliver theobald
best for absolute beginners
As the title suggests, this book offers a basic introduction to machine learning for beginners with no prior knowledge of coding, math, or statistics. Theobald’s book goes step by step, written in plain language, with images and explanations accompanying each machine learning algorithm.
if you are completely new to machine learning and data science in general, this is the book for you.
3. machine learning for hackersby draw conway and john myles white
See Also: Paperback vs Hardcover: What&039s the Difference? 2022
best for programmers (who enjoy practical case studies)
Instead of the term “hackers” referring to people who gain unauthorized access to people’s data, the authors speak of programmers who hack code for a specific purpose or project. This book is ideal for those with programming and coding experience but less familiar with the mathematical and statistical side of machine learning.
The book uses case studies that offer practical applications of machine learning algorithms, helping to situate mathematical theories in the real world. examples, such as how to create a recommendation on who to follow on twitter, keep abstract concepts grounded.
4. hands-on machine learning with scikit-learn, keras, and tensorflow by geron aurelien
best for those who know python
If you already have experience with the Python programming language, this book provides further guidance on understanding the concepts and tools you’ll need to develop intelligent systems. Hands-On Machine Learning is written so that each chapter includes exercises to apply what you have learned in previous chapters.
Use this book as a resource for developing project-based technical skills that can help you land a job in machine learning.
read more:what is python used for? a guide for beginners
5. deep learning by ian goodfellow, yoshua bengio, and aaron courville
best book on deep learning
This book offers a beginner’s introduction for those who are more interested in the deep learning aspect of machine learning. Deep Learning explores key concepts and topics in deep learning, including linear algebra, probability and information theory, and more.
bonus: the book is accompanied by lecture slides on their website and exercises on github.
read more: what is deep learning? definition, examples and races
6. an introduction to statistical learning by gareth james, daniela witten, trevor hastie, and robert tibshirani
best for a statistical approach
This book is an excellent tool for those who already have some knowledge of statistics. You will be able to understand statistical learning and discover the process of managing and understanding complex data sets. covers important concepts like linear regression, tree-based models, and resampling methods, and includes many tutorials (using r) for applying these methods to machine learning.
7. programming collective intelligenceby toby segaran
the best guide for practical application
As you delve deeper into machine learning, this book will teach you how to create algorithms for specific projects. is a practical guide that can teach you how to customize programs that access data from websites and other applications and then collect and use that data. Ultimately, she’ll be able to create the algorithms that detect patterns in the data, such as how to make predictions for product recommendations on social media, match singles on dating profiles, and more.
See Also: 264 Free Audiobooks of Popular Books Narrated on Youtube
read more: 7 machine learning algorithms to learn about
8. Fundamentals of Machine Learning for Predictive Data Analyticsby John D. kelleher, brian mac namee and aoife d’arcy
best for analytical approach
another book that provides practical applications and case studies along with the theory behind machine learning. This book is written for those who develop on and with the Internet. takes the guesswork out of predictive data analysis and provides a comprehensive collection of algorithms and models to apply machine learning.
read more: what is data analysis?
9. machine learning for humans by vishal maini and samer sabri
the best for a free resource
The latter is an electronic book that can be downloaded for free [2]. is a clear, easy-to-read beginner’s guide to machine learning, complete with code, math, and real-world examples for context. In five chapters, you’ll learn why machine learning matters, and then become familiar with supervised and unsupervised learning, neural networks and deep learning, and reinforcement learning. as a bonus, it includes a list of resources for further study.
if fiction is more your style, isaac asimov’s classic i, robot imagines how humans and robots would fight to survive together. other science fiction authors like ted chiang explore our relationship with artificial intelligence technology in stories like the life cycle of software objects.
get started with machine learning.
machine learning can lead to careers such as: machine learning engineer, data scientist, human-centered machine learning designer, computational linguist, software engineer/developer, and business intelligence developer.
If you’re interested in machine learning, Stanford University’s machine learning major can give you the skills to jumpstart your career. You’ll gain an understanding of supervised and unsupervised learning, as well as best practices and case studies for a comprehensive introduction to machine learning.
related articles
-
12 Data Analytics Books for Beginners: A Reading List for 2022
what is a machine learning engineer and how do you get started?
machine learning models: what they are and how they are made
7 machine learning algorithms to learn about
article sources
1. of course. “the best jobs in the us in 2019, https://www.indeed.com/lead/best-jobs-2019”. accessed June 2, 2022.
2. vishal maini. “machine learning for humans”, https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12. accessed June 2, 2022.
-