Building a Simple Linear Classifier in Python

Have you ever wondered how machine learning algorithms actually make decisions? Behind complex AI models lies simple, elegant mathematics—starting with the humble linear function. This text-only course guides you through the process of building a foundational linear classifier from scratch. You will learn how to represent data, structure decision boundaries, and write clean Python code to adjust decision lines algorithmically. What you'll learn: - Understand the foundational mathematics of linear functions and decision boundaries - Represent training data in Python using clean, modern structures like type hints and dataclasses - Implement a basic classification algorithm to categorize data points into distinct groups - Apply iterative line adjustment techniques to train your classifier step by step - Analyze classification errors and refine your algorithm for better accuracy You will start with core terminology and mathematical definitions before moving on to practical coding. Through clear written explanations and structured code snippets, you will build a working classifier and learn how to optimize it. This course is designed for beginner programmers and aspiring data specialists who want to understand the inner workings of machine learning without getting lost in complex libraries. No prior machine learning experience is required; basic Python knowledge is all you need. Step into the world of machine learning and write your first classification algorithm today.