First, in this class I cover basic topics about machine learning, including the types of machine learning models you might encounter and give examples of each type of model.

Second, I cover the essential topics one needs to know to pre-process data before doing a machine learning analysis. We discuss the concepts of one-hot encoding/dummy coding, rescaling continuous features, imputing missing values, and model validation techniques.

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Code Available Here and Data Available Here

Suggested Reading:

• Python Machine Learning, 3rd Edition, Chapter 1 (p. 1-17) and Chapter 4 (p. 109-127)
• Hands on Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition, Chapter 1 (p. 1-34) and Chapter 2 (p. 62-72)
• Feature Scaling for Machine Learning
• Ordinal and One-Hot Encodings for Categorical Data