Applied ML I: Supervised Learning

О курсе

In this course, you will learn how to translate real business problems into well-defined supervised machine learning tasks and select the right algorithms to solve them. You will build and optimize linear models, design and tune tree-based methods, apply SVMs with powerful kernels, and explore ensemble techniques that boost accuracy and robustness. Along the way, you will gain hands-on experience implementing models with Python and scikit-learn, evaluating performance, and understanding the trade-offs that guide effective model selection. By completing this course, you’ll develop the practical skills needed to approach ML problems with confidence—from framing objectives to choosing models that align with data constraints and business goals. What makes this course unique is its blend of conceptual foundations, business-driven reasoning, and end-to-end applied workflows drawn from multiple expert instructors. Whether you’re preparing for ML roles or expanding your data science toolkit, this course provides a clear, structured path to mastering supervised learning techniques used across modern industry.

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