Mathematics of Regression: Linear Models and Gradient Descent in Python

To truly master machine learning, you must understand the mathematical engines driving your algorithms. Linear regression and gradient descent are the essential building blocks that turn raw data into predictive insights. This text-based course guides you through the core mathematical concepts of data science from the ground up. You will transition from reading foundational theories to writing clean Python scripts that implement simple and multiple linear regression, optimizing your models with gradient descent and evaluating them with industry-standard libraries. What you'll learn: - Understand the core algebraic and calculus principles behind machine learning models. - Implement simple and multiple linear regression models using Python, pandas, and scikit-learn. - Master the mechanics of gradient descent to optimize model parameters. - Write clean, type-hinted Python code to handle data preprocessing and model evaluation. - Evaluate model performance using metrics like Mean Squared Error and R-squared. - Analyze datasets with structured, step-by-step written exercises. You will start with key mathematical terminology and fundamental definitions before moving into hands-on code implementations. Through clear written explanations and practical code snippets, you will learn how to prepare data, train models, and tune parameters. This course is designed for aspiring data scientists and beginners with basic Python knowledge who want to understand the math behind machine learning without complex academic jargon. Start reading today to unlock the mathematical foundations of data science.