Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.

By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.

Table of Contents

1. Journey from statistics to machine learning
2. Parallelism of statistics and machine learning
3. Logistic regression versus random forest
4. Tree-based machine learning models
5. K-nearest neighbors and naive Bayes
6. Support vector machines and neural networks
7. Recommendation engines
8. Unsupervised learning
9. Reinforcement learning