The Supervised Machine Learning Course Description
This course helps you study the theory underlying each of the six algorithms before utilizing Python’s sci-kit and learn package to apply your knowledge to real-world case studies that are specific to each method.
Here’s what you will learn in this book:
- Introduction
- Setting up the Environment
- Naive Bayes
- K-Nearest Neighbors
- Decision Trees and Random Forests
- Support Vector Machines
- Ridge and Lasso Regression