Best 7 Self-Study Machine Learning Books For Beginners
Nowadays, the field of machine learning is gaining popularity among students. Various students want to make their careers in this field. However, studying machine learning is not as simple as we think. There are lots of concepts we have to learn. Also, there are lots of Machine Learning books we have to read.
However, if you are also thinking of making your career in Machine Learning, that is a good idea. But, there are lots of contents and concepts you have to study. But you can achieve this goal by doing hard work and practice. Besides studying in college or university, you have to do self-study as well. There are various best machine learning books available for beginners in the market. You can get extra knowledge outside the classroom from these books and masters in machine learning and get good grades also.
So, let’s get started!
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Best Machine Learning Books For Beginners
To help you, we are providing some of the best books on machine learning. Following is the list of them. Let’s have a look at them;
The Hundred-Page Machine Learning Book
Author Name: Andriy Burkov
It is the ideal book for beginners if they want to learn about Machine Learning. However, Is it possible to cover all aspects of machine learning in under 100 pages? This book by Andriy Burkov is an attempt to do the same. Also, this book is easy to read and understand.
Topics Covered
- Supervised Learning and Unsupervised Learning
- Anatomy of a learning algorithm
- Neural Network and Deep Learning
- Fundamental Algorithms
Machine Learning For Hackers
Author Name: Drew Conway and John Miles White
This book is for experienced programmers who want to analyze data. In this book, the hackers mean adroit mathematicians. Because the book is all about data analysis in R. This book is one of the best machine learning books for those who are familiar with the R language. In addition, the book also goes through how to use advanced R for data wrangling.
Moreover, the best part of this book is it includes relevant case examples that show the need of using machine learning algorithms. Also, despite digging more into the mathematical theory of machine learning, the book uses a variety of real-world examples to make ML easier and faster.
Topic Covered
- Using R for querying data
- Linear Regression
- Developing a Naive Bayesian classifier
- Optimization Techniques
Machine Learning
Author Name: Tom M. Mitchell
This book is the best book to start if you are new to machine learning. It provides a thorough explanation of machine learning theorems. Also, it has pseudocode descriptions of the essential algorithms. In addition, this ML book has various examples and case studies that make ML simple to learn and understand.
Therefore, this is one of the must-have machine learning books for anybody interested in pursuing a career in machine learning.
Topics Covered
- Reinforcement Learning
- Genetic Algorithms
- Also, machine learning concepts and Techniques
- Inductive logic programming
- Introduction to basic machine learning approaches
The Elements Of Statistical Learning: Data Mining, Inference, and Prediction
Authors Name: Trever Hastie, Robert Tibshirani, and Jerome Friedman
This book is best for those who enjoy statistics and want to understand machine learning from a statistical perspective. Moreover, this book focuses on mathematical equations That define the essential logic of an ML algorithm. Also, make sure you have a basic knowledge of linear algebra before starting this book.
Topics Covered
- Supervised and Unsupervised Learning
- Ensemble Learning
- Also, Random Forests
- High-dimensional problems
- Neural Networks
- Model inference and averaging
- Linear methods for classification and regression
Learning From Data: A Short Course
Author Name: Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin
Want to learn everything about machine learning in a shorter amount of time? Also, do you have a proper understanding of engineering mathematics? However, this book is a perfect way to start. Also, this book teaches its readers to better understand complicated machine learning concepts.
Moreover, The Learning from Data: A Short Coursebook offers brief, to-the-point explanations for long and confusing answers. Therefore, we can say that this book is one of the best Machine Learning Books.
Topics Covered
- Validation
- Error and Noise
- Support Vector Machines
- Also, Kernel Methods
- Overfitting
- Regularization
- Radial Basis Functions
Pattern Recognition And Machine Learning
Author Name: Christopher M. Bishop
This book is a great resource for learning and applying statistical approaches in machine learning and pattern recognition. Also, this book requires a solid knowledge of linear algebra and multivariate calculus.
Moreover, this book includes extensive practice problems that provide a thorough overview of statistical pattern recognition techniques. Also, the book uses graphical models to describe probability distributions in a novel way.
Topics Covered
- New models based on kernels
- Approximate inference algorithms
- Introduction to machine learning and pattern recognition
- Introduction to basic probability theory
- Bayesian Methods
Bayesian Reasoning And Machine Learning
Authors Name: David Barber
This is one of the must-have machine learning books for anybody interested in the field of machine learning. Moreover, the book is an excellent choice for computer scientists who want to understand ML. But, it lacks a great basis in calculus and linear algebra.
Moreover, the Bayesian Reasoning and Machine Learning book are full of well-explained examples and exercises. This makes the book suitable for both undergraduate and graduate students of computer science.
Topics Covered
- Probabilistic Reasoning
- Approximate Interference
- Naive Bayes Algorithm
- Also, Dynamic Models
- Graphical Models Framework
- Learning in probabilistic models
Final Words
However, we have discussed the various best 7 machine learning books in this blog. Therefore, you have to pick up the book you want to learn. All these books are good for beginners. In addition, there are various online tutorials, courses, youtube videos, etc are also available on the internet. You can gain knowledge from that source too besides reading books. Lastly, I hope this blog helps you in finding the best book for machine learning.
