Simple Features with Bag of Words for Machine Learning

A short addendum to the feature engineering (for text) lecture from the fall 2019 Machine Learning course at UW's Professional Maters Program. The video give two additional examples of concepts from the lecture. 1) is an example of Bag of Words features; 2) explains feature selection at training time vs at performance time.

The full lecture includes many more feature engineering techniques and an overview of ROC curves for evaluating machine learning models and an overview of and operating points, a key technique for leveraging machine learning in practice.

[gcklo]

M2 has 2 a’s. Shouldn’t the count be 2?