Machine Learning Course Introduction

The following video was used in my JMU classes, not specifically designed for this website.

Goals of the Course

Upon completion of this course, students are expected to

  • Understand basic concepts and applications of machine learning models;

  • Learn different machine learning models and their strengths and weaknesses, including:

    • Simple Regression;

    • Multiple Regression;

    • Logistic Regression;

    • K-nearest Neighbors;

    • Naïve Bayes;

    • Decision Trees;

    • Neural Networks;

    • Natural Language Processing;

    • Applications of Artificial Intelligence.

  • Design and apply machine learning models with Python, RapidMiner, and Amazon Web Services (AWS).

Textbook

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Müller, A. C., and S. Guido. 2016. Introduction to Machine Learning with Python. O'Reilly Media, Inc. Online version

 

Grus, Joel. 2019. Data Science from Scratch: First Principles with Python. 2nd edition. Sebastopol, CA: O'Reilly Media. Online version

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