Machine Learning Simplified:
A gentle introduction to supervised learning

The underlying goal of "Machine Learning Simplified" is to develop strong intuition for ML inside you. We would use simple intuitive examples to explain complex concepts, algorithms or methods, as well as democratize all mathematics behind machine learning.

After reading this book, you would understand everything that comes into the scope of Supervised ML, and would be able to not only understand nitty-gritty details of mathematics behind the scene, but also explain to anyone how things work on a high level.

The book is free, but you can purchase EPUB version through Amazon or show your appreciation to the author and purchase PDF through Leanpub.

Table of contents:
I. FUNDAMENTALS OF SUPERVISED LEARNING
Chapter 1. Introduction
Chapter 2. Overview of Supervised Learning
Chapter 3. Model Learning
Chapter 4. Basis Expansion & Regularization
Chapter 5. Model Selection
Chapter 6. Feature Selection
Chapter 7. Data Preparation
II. ADVANCED SUPERVISED LEARNING ALGORITHMS (WIP)
Chapter 1. Regression Models
Chapter 2. Logit Models
Chapter 3. Bayesian Models
Chapter 4. Maximum Margin Models
Chapter 5. Tree-Based Models
Chapter 6. Ensemble Models
Chapter 7. Algorithms Selection
Chapter 8. Hyperparameter Tuning
Chapter 9. Evaluation Metrics

Read for free: https://themlsbook.com/read
Buy on Amazon: https://www.amazon.com/dp/B0B216KMM4/qid=1653304321
Buy on LeanPub: https://leanpub.com/themlsbook
Repository: https://code.themlsbook.com/

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