In this course,
First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work.
Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated.
Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks. When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution technique for your use-case.