Numerical Computing with Python

What this book covers

Chapter 1, Journey from Statistics to Machine Learning, introduces you to all the necessary fundamentals and basic building blocks of both statistics and machine learning. All fundamentals are explained with the support of both Python and R code examples across the chapter.

Chapter 2, Tree-Based Machine Learning Models, focuses on the various tree-based machine learning models used by industry practitioners, including decision trees, bagging, random forest, AdaBoost, gradient boosting, and XGBoost with the HR attrition example in both languages.

Chapter 3, K-Nearest Neighbors and Naive Bayes, illustrates simple methods of machine learning. K-nearest neighbors is explained using breast cancer data. The Naive Bayes model ...