Machine learning (ML) is the study of computer algorithms and statistical models computer systems use to carry out different tasks without explicitly getting any instructions.
The field of machine learning is now growing day by day with its advanced implementations. The concepts are very easy to grasp, and anyone can learn them with a little focus.
This article will give you an overview of the best machine-learning books for beginners. Here’s a list of the books:
- Machine Learning For Absolute Beginners
- Machine Learning For Dummies 1st Edition
- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies 1st Edition
- Programming Collective Intelligence
- Machine Learning for Hackers
- Machine Learning in Action 1st Edition
- Data Mining: Practical Machine Learning Tools and Techniques
- The Hundred-Page Machine Learning Book
- Machine Learning
- The Elements Of Statistical Learning: Data Mining, Inference, and Prediction
- Learning from Data: A Short Course
Machine Learning For Absolute Beginners
Oliver Theobald wrote this book. It is the best source of information for you if you want a complete introduction to machine learning for beginners. The author uses very simple and plain language so that anyone can easily grasp the concept. Visual explanations and examples are also added to ensure that the reader will not have difficulty understanding different algorithms. Some simple programming techniques are also introduced to understand machine learning better.
Machine Learning For Dummies 1st Edition
John Paul Mueller and Luca Massaron wrote this book. It aims to put forth all the basic concepts and theories of machine learning in such a way that readers understand. It also highlights the use of machine learning concepts and theories in the practical world. The book also involves coding in Python to let the machines find patterns and analyze results.
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies 1st Edition
John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy wrote this book. It is a complete guide for beginners who want to dive deep into machine learning. The book covers all the fundamental concepts, theories, examples, and practical applications with working examples. It elaborates on all the basic concepts with practical learning approaches, algorithms, and models for its readers.
Programming Collective Intelligence
Toby Segaran wrote this book. It focuses more on the practical application of machine learning strategies to create algorithms for gathering data for projects. You will find the techniques to create programs for accessing and collecting data from different websites and applications. The book also presents filtering techniques, methods to detect groups or patterns, search engine algorithms, ways to make predictions, and much more.
Machine Learning for Hackers
Drew Conway and John Myles White wrote this book. The author used the word ‘Hacker’ for the ones who hack the codes to achieve specific goals and practical projects. It is the best source of information for someone very familiar with programming and coding techniques. Machine learning is linked with different mathematical principles, so if a person is not very good at mathematical techniques but is familiar with coding and programming techniques, many hands-on case studies are showcased in this book to present the material in real-world practical applications. It also presents the solution to the typical problems in machine learning by using the R programming language.
Machine Learning in Action 1st Edition
This book was written by Peter Harrington. It is the best guide for beginners who want to grasp the basic concepts behind the practical knowledge of machine learning. You will find different programming and coding techniques that help you develop and code your own programs to get data for analysis. If you are familiar with Python programming, it will be much more helpful to understand the concepts and examples in this book.
Data Mining: Practical Machine Learning Tools and Techniques
This book was written by Ian H. Witten, Eibe Frank, and Mark A. Hall. It focuses more on technical details for machine learning. You can learn about how to obtain data from particular mining techniques. Moreover, you will find all the major and minor technical details for machine learning and data gathering and evaluation methods under machine learning.
The Hundred-Page Machine Learning Book:
This book was written by Andriy Burkoy. It is written in a very simple and easy-to-read text. After reading this book, you will be able to learn and develop complex AI systems, qualify for an ML-based interview, and you can even start your own business based on machine learning. So, if you are looking for a machine learning guide, this book is suitable for teaching you the basics of machine learning.
Tom M. Mitchell wrote this book. It covers all the fundamentals of machine learning, including the theorems with pseudocode summaries of the respective algorithms. To make the algorithms understandable, you will find a lot of examples and case studies in this book. If you are really thinking about making your career in the field of machine learning, then this book is a must for you.
The Elements Of Statistical Learning: Data Mining, Inference, and Prediction:
Trevor Hastie, Robert Tibshirani, and Jerome Friedman wrote this book. It is a perfect guide for those who are interested in stats and want a statistical perspective of machine learning. The book focuses more on mathematical derivation to teach the concepts of ML algorithms. In short, this book is beginner-friendly if you have a good knowledge of mathematics and basic linear algebra.
Learning from Data: A Short Course:
Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin wrote this book. It does not take its readers into the complexities of machine learning; rather it focuses on the basic concepts to simplify the complex techniques of machine learning. That is why it could be the best source of learning for beginners in the field of machine learning.
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