“If you don’t like to read, you haven’t found the right book.” — J.K. Rowling
Well, this is absolutely true in my case. I had never read a book outside of my school curriculum ever before I stumbled upon the Harry Potter series. The books beautifully depict the adventurous journey of little novice magicians to become powerful wizards.
Following the same chain of thoughts, I present to you a collection of books which one must read to get on the journey to become an expert magician of machine learning and data science.
- Think Stats Paperback by Allen B. Downey
Statistics lays the foundation of machine learning and data science. This book introduces and details about the rules of probability and probability distributions, data visualization techniques, and statistical methods including hypothesis testing and correlation.
Every chapter has a case study with python code snippets for better understanding of concepts.
Download link: http://greenteapress.com/thinkstats/thinkstats.pdf
2. Automate the Boring Stuff with Python by Al Sweigart
A fun book for quickly learning python programming for total beginners. This book will make your life easy by helping you automate mundane tasks like searching for a string in a pdf or send reminder emails or format data in excel etc.
Download link: https://automatetheboringstuff.com/
3. Python Machine Learning by Sebastian Raschka
The book covers the Machine Learning concepts along with simple mathematical details and python code snippets using Scikit-Learn libraries.
A good read for everyone — if you have already studied machine learning theory in detail, this book will show you how to put your knowledge into practice. If you have used machine learning techniques before and want to gain more insight into how machine learning really works, this book is for you. Don’t worry if you are completely new to the machine learning field; you have even more reason to be excited.
4. Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee and Aoife D’Arcy
An introductory text on the fundamentals of machine learning, and the applications of machine learning. Chapters in this book begin with a big idea explaining the concept intuitively and building on to the technical details gradually. This book also has many real-time case studies showcasing the whole lifecycle of a data science project.
5. An Introduction to Statistical Learning with R by Trevor Hastie and Rob Tibshirani: A must-read for a broader understanding of ML concepts. This book explains the topics in great detail with simplicity, detailing from assumptions to high-level mathematical details, to interpreting outputs to applications of the concepts, along with a lab section in R with every chapter.
6. Introduction to Data Science by Rafael A Irizarry
This book introduces concepts and skills to tackle real-world data analysis challenges. It provides a simple introduction to all the essential concepts ranging from probability, statistical inference, linear regression and machine learning. It covers basics of R programming, packages like dplyr, ggplot2, caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown.
Download link: https://rafalab.github.io/dsbook/
7. Advanced R by Hadley Wickham
This book is a must to read for anyone wishing to write efficient and faster codes in R. This book is for advanced intermediate R programmers and for programmers transitioning to R from other languages. A good book to read after having read the Introduction to Data Science book which covers the basics of R programming.
Download link: https://englianhu.files.wordpress.com/2016/05/advanced-r.pdf
8. Deep Learning with Python by FRANÇOIS CHOLLET
This book is for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. This book offers a practical, hands-on exploration of deep learning. It focuses more on to explain the quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. The code examples use the Python deep-learning framework Keras, with Tensor-Flow as a backend engine. Personally, this is one of the few technical books that I enjoyed reading, just like reading the Harry Potter series as a kid.
9. Machine Learning Yearning by Andrew Ng
This book focuses on how to make the ML algorithms work efficiently. The book does not talk about the ML concepts in detail, but explains how to structure the machine learning project development, details the best practices and common pitfalls along with case studies. Andrew Ng gives all the important tips on troubleshooting a machine learning system in real life.
Download link: https://www.deeplearning.ai/machine-learning-yearning/
10. The Art of Data Science by Roger D. Peng and Elizabeth Matsui
The Art of Data Science” dives into the practice of exploring and finding discoveries within any lake of data at your fingertips. It focuses on the process of analysing data and filtering it down to find the underlying stories. The authors use their own experiences to coach both beginners and managers through analysing data science.
Hope you find this list useful.
Please share your feedback about these books and suggest more books for all of us to read!!
Originally published at https://www.edvancer.in on June 15, 2019.