Data science and machine learning areas have become very popular in recent years. Statistics and mathematics are taking their place in literature. Although data science is not a very new term, it has become an area of interest for everyone thanks to open libraries available to researchers. As in every field, a foundation is needed to understand the theoretical part of this field. It will form the basis, and I will share some sources considered reliable in my machine learning adventure. Of course, there may be more different books on your list. So let’s start! Have a good reading!
1. Data Mining Concepts and Techniques
Authors: Jiawei Han, Micheline Kamber, Jian Pei; Morgan Kauffman Publishing
It is at the beginning of the theoretical contents that explain the basis of the concepts and techniques in the literature. It is a resource that you can frequently refer to in your academic studies. You can theoretically learn the structure of data science and machine learning. The book, which includes information about the system of data, data processing, data types, machine learning types, and algorithms, has a cult characteristic. I recommend reading the book repeatedly for understanding. There are no codes for the book application. The concept of the book is not suitable for this anyway. Theoretically, it is a helpful resource.
2. An Introduction to Statistical Learning
Authors: Gareth James, Daniela Witten, Trevor Hastie, Rob Thibshrani; Springer Publishing
The basis of machine learning is mainly statistics. The book focuses not on the fundamentals of statistics but its structure used in machine learning. In other words, the book’s foundation is based on theoretically and practically explaining the basics of statistics used in data science and machine learning. It is one of the other cult books in this field. It is a formula-based book. One of the main advantages of this book is that it includes applications on R, one of the popular data science programming languages. It is a book that you can choose both theoretically and practically. I recommend adding it to your library.
3. Principles and Theory for Data Mining and Machine Learning
Authors: Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang; Springer Publishing
This book covers many methods in data mining and machine learning. The best thing to me is that it tells each story from a theoretical way, but not a superficial way. It really helps you understand these machine learning methods from a deep perspective. It is one of the other theoretically based books. I do not recommend reading this book without thoroughly reading the above two books. Because there is information transfer on subjects that are difficult to understand statistics, such as parametric and non-parametric, the other part of the book covers the basic types of machine learning and the basics of algorithms.This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.
4. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Authors: Aurelien Geron, O’Reilly Publishing
Yes, it’s time for my favorite book! Of course, all books contain quality information at the core. However, this book is at the top of my list! You ask why? Let’s take a quick look at the content!
Firstly, the book focuses more on machine learning and covers machine learning types and algorithms in depth. My favorite feature is that it teaches the basics of machine learning to those who read it plainly and simply. Also, in the book, you can find many examples performed in Python. It is rich in theoretical and applied knowledge on regression and classification. Author also talks about the concept of deep learning and provides examples. It is a tremendous theoretical book on deep learning, covering many topics. The writing is clear, the maths not too difficult if you concentrate, and it is pretty self-contained. It is also possible to find the author’s videos on machine learning on YouTube. This book is for you if you have a basic knowledge of Python and basic statistics! By following the applications, you can create a machine learning adventure for yourself!
5. Deep Learning (Adaptive Computation and Machine Learning Series)
Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville; MIT Press
Although the last book I will recommend is titled deep learning, it is a work that teaches algorithms based on deep learning, starting with statistics, which is the basis of the subject, establishing a context for machine learning, and then leading deep learning-based algorithms. It’s one of the main Deep Learning reference works, comprehensive, and expertly written.You can also find explanations and usage of formulas for machine learning and deep learning in it. However, since the mathematical content is intense, it is helpful to read after machine learning and statistics are well learned. I recommend that you read the book slowly and with an understanding of the formulas. Although it is impossible to understand all the formulas at the beginning, it is helpful to know that no researcher has mastered the subject by memorizing them. The goal is to understand logic and function.
I hope these recommended books will be useful in your machine learning adventure, good luck to you all! Feel free to contact with any questions! See you in the next post!