01: The Basics of Machine Learning
In this section, you will be introduced to the core concepts of machine learning, laying a strong foundation for more advanced topics. You’ll start by understanding what machine learning is and how it relates to basic calculations and data manipulation. You’ll explore different types of data distributions, specifically normal and uniform, and see how they influence models.
Through regression analysis, you'll learn about linear, polynomial, and multiple regression, understanding how these models make predictions based on data. You'll also cover how to evaluate model accuracy using train/test splits and the R-squared value, a key performance indicator in regression tasks. Lastly, you’ll dive into classification performance measurement with confusion matrices, allowing you to assess how well your model performs in predicting categorical outcomes. By the end of this chapter, you’ll have a solid grasp of the fundamentals needed to build, evaluate, and improve machine learning models.