With the growing popularity of artificial intelligence, understanding the fundamentals of machine learning is becoming increasingly important. It’s from simple models that the path to the more complex systems that drive technology development today begins. This section is designed as a guide to the fundamentals of ML—from linear regression, through classification methods, to clustering. Each topic is presented in a straightforward manner, with an emphasis on intuition and practical applications.

Below are links to sections where I discuss, step by step, specific topics in an accessible way.

Linear Regression

Fit Slope

R Squared & Testing assumptions

Nearest neighbors & distances

Support Vector Machine

Clustering K_Mean K_Shift