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1️⃣ Natural Language Processing with Python
📌 Analyzing Text with the Natural Language Toolkit.
2️⃣ Think Stats
📌 This book covers many of the core statistical concepts for data science including data analysis, distributions, and probability.
3️⃣ Bayesian Methods for Hackers
📌 This book bridges the gap between theoretical Bayesian machine learning methods.
4️⃣ R for Data Science
📌 This book is one of the best introductions to learning R for data science.
5️⃣ Machine Learning Yearning
📌 This book contains practical steps and frameworks for successful machine learning projects by Andrew Ng.
6️⃣ Hands-on Machine Learning with Scikit-learn & Tensorflow
📌 This book gives a very good overview of the machine learning process with Scikit-learn and Tensorflow.
7️⃣ Forecasting: Principles and Practice
📌 This book offers a very comprehensive overview of methods used for forecasting.
8️⃣ Deep learning
📌 This book is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
9️⃣ Linear Algebra
📌 This book covers the foundational concepts that would usually be covered in a typical undergraduate course.
🔟 Introduction to Machine Learning with Python
📌 This book focuses on the practical application of machine learning techniques rather than covering the math behind the field.
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