Data Science: Regression and Clustering Models

Extracting meaningful patterns from data is the core of modern decision-making. This text-only course introduces you to the essential concepts of regression and clustering, the two pillars of supervised and unsupervised machine learning. You will transition from understanding basic statistical concepts to building, validating, and refining predictive models. By studying real-world scenarios through written explanations and clean code examples, you will gain the confidence to apply these techniques to your own datasets. What you'll learn: Understand the foundational mathematics and core terminology of regression and clustering; Build and refine linear and multi-variable regression models using Python and R; Apply cross-validation techniques to prevent overfitting and ensure model reliability; Group complex datasets using unsupervised clustering algorithms like K-Means; Evaluate model performance using modern metrics and cloud-based machine learning tools like Azure ML; Implement clean, reproducible data workflows using modern dataframe libraries. The course begins with foundational definitions and data preparation basics before moving into hands-on regression modeling, validation strategies, and clustering techniques. You will progress systematically through structured text explanations, code walkthroughs, and conceptual exercises. This course is designed for aspiring data scientists, analysts, and beginners looking for a solid conceptual and practical starting point in machine learning, with no prior modeling experience required. Start reading today to master the core models of data science.