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This course provides a detailed executive-level review of contemporary topics in supervised machine learning theory with specific focus on predictive modeling and linear regression. The ideal student is a technology professional with a basic working knowledge of machine learning theory. Additionally, to better inform the interested student, the final lesson of this course presents samples in Python describing the essential implementation of described regression methods. To reduce space and improve clarity, this code targets a basic Keras environment - this inclusion is not meant as an endorsement of one system over another (all provide benefits); instead, at the time of this writing, Keras simply offers a popular, facile 'frontend' for managing TensorFlow or Microsoft Cognitive Toolkit deep learning systems, all using this popular script. As an 'executive review', this text presents a distillation of essential information without the clutter of formulae, charts, graphs, references and footnotes. Thus, the student will not have a 'textbook' experience (or expense) while reviewing its contents. Instead, the student will quickly pass through a surprising wealth of actionable, easily-digestible technological information without the distraction of extemporaneous considerations.