Classifying Non-Linear Data with SVM Kernels and Logistic Regression

Linear boundaries often fail when real-world data is complex and circular. Understanding how to apply non-linear classification techniques is a vital skill for any aspiring data scientist. This text-based course guides you through the process of generating synthetic datasets, analyzing classification boundaries, and comparing the performance of Logistic Regression against Support Vector Machines (SVM) with various kernel functions. You will gain a deep, intuitive understanding of how different algorithms interpret non-linear structures. What you'll learn: - Understand the fundamental concepts and math behind Logistic Regression and Support Vector Machines. - Generate and manipulate synthetic circular datasets using modern Python libraries. - Apply different SVM kernels, including Linear, Polynomial, and Radial Basis Function (RBF), to map complex decision boundaries. - Compare model performance using modern classification metrics and evaluation techniques. - Implement clean, maintainable machine learning code using Python type hints and current scikit-learn workflows. - Analyze decision boundaries conceptually to choose the right algorithm for non-linear data patterns. You will start with core theoretical definitions and foundational concepts before moving on to step-by-step code walk-throughs and model comparison exercises. This course is designed for beginner data scientists, analysts, and programmers who want to transition from basic linear models to advanced classification techniques. Start reading today to master decision boundaries and elevate your machine learning toolkit.