Data science is a rapidly growing field, and there are many great books available to help you learn the basics and beyond. In this video, we will discuss some of the best books to learn data science for beginners and experts.
For beginners:
Python for Data Analysis by Wes McKinney is a great introduction to data science using Python. It covers the basics of Python programming, as well as data analysis techniques such as data wrangling, cleaning, and visualization.
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron is a great introduction to machine learning using Python. It covers a wide range of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is a classic introduction to statistical learning. It covers a wide range of statistical concepts, as well as machine learning algorithms.
For experts:
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman is a more advanced book on statistical learning. It covers a more in-depth treatment of statistical concepts, as well as machine learning algorithms.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy is a comprehensive book on machine learning from a probabilistic perspective. It covers a wide range of machine learning algorithms, as well as the underlying statistical principles.
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a comprehensive book on deep learning. It covers the basics of deep learning, as well as advanced topics such as convolutional neural networks and recurrent neural networks.
These are just a few of the many great books available to learn data science. The best book for you will depend on your specific needs and experience level.
