Simple Linear Regression and Gradient Descent in Python

Understanding how variables relate to one another is the cornerstone of predictive modeling and data science. This text-based course guides you through the process of building simple linear regression models from scratch and optimizing them using gradient descent. You will transition from a beginner to a confident practitioner who can translate mathematical concepts into clean, functional Python code, evaluating your models with modern data practices. What you'll learn: - Understand the foundational mathematics behind linear relationships and regression lines. - Implement the gradient descent algorithm from scratch in Python to find optimal model parameters. - Calculate key evaluation metrics like Mean Squared Error and R-squared to assess model performance. - Write clean, maintainable Python code utilizing modern practices like type hints and structured programming. - Apply your models to real-world datasets to make accurate, data-driven predictions. The course begins with essential terminology and the mathematical theory of linear equations before guiding you step-by-step through writing the optimization code, testing your algorithms, and evaluating your results. It is designed for beginners in data science; a basic familiarity with Python variables and loops is helpful, but no prior background in advanced mathematics or machine learning is required. Start reading today to unlock the core mechanics of predictive machine learning.