In this course, instructor Lyron Andrews shows you how to describe the system components of machine learning (ML) and their function in the AI ecosystem. This includes identifying general, supervised, unsupervised, and semi- or self-supervised learning, along with enumerating the steps related to the ML pipeline. Along the way, gather insights for evaluating the need for anomaly detection and dimensionality reduction related to training models. Lyron outlines the steps of the machine learning pipeline, from data acquisition and preparation to modeling, verification, and validation. By the end of this course, you’ll be prepared to choose the right ML approach to meet the unique needs of your business case.
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