Logistic Regression and Linear Decision Boundaries

Understanding how a classification model separates different classes is fundamental to mastering machine learning. This text-based course guides you through the mathematical and practical concepts of linear decision boundaries in logistic regression. By working through structured explanations and code-based exercises, you will transition from understanding basic classification theory to confidently fitting models and representing decision boundaries using Python. What you'll learn: - Understand the foundational mathematics behind logistic regression and binary classification - Generate synthetic datasets to test and evaluate your classification models - Fit logistic regression models using modern Python libraries and clean code practices - Calculate the linear decision boundary mathematically using model coefficients - Analyze classification performance and decision thresholds through code outputs - Apply type hints and structured programming to write robust machine learning scripts The course begins with core definitions and the mathematical foundations of logistic regression. You will then progress to generating synthetic data, training models, and calculating decision boundaries step-by-step through written explanations and code examples. This course is designed for beginners in machine learning and data science. A basic familiarity with Python is helpful, but no prior background in advanced statistics is required. Start reading today to demystify how logistic regression makes classification decisions.