An Introduction to Statistical Learning
This book provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
https://hastie.su.domains/ISLR2/ISLRv2_website.pdf
Python Data Science Handbook
You’ll learn how to use the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. This resource is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.
https://jakevdp.github.io/PythonDataScienceHandbook/
Dive into Deep Learning
Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 400 universities from 60 countries
Approaching (Almost) Any Machine Learning Problem
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is for you if you are looking for guidance on approaching machine learning problems.
https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf
