Feature Learning with Matrix Factorization and Neural Networks

A major step in most predictive analytics workflows is to create features from input data that can be fed into machine learning algorithms. This is often a manual and labor-intensive effort. Feature learning (also known as representation learning) allows important features to be automatically extracted from raw input data.

Topics that are covered:

• Manual feature engineering vs. feature learning
• Example applications of feature learning
• Matrix factorization approaches (deep dive into PCA/SVD)
• Neural network approaches (deep dive into Autoencoders and Skip-Gram/Word2Vec)
• Code samples using scikit-learn and keras

The make-data.ipynb does NOT need to be run, the feature-learning.ipynb pulls a pre-processed dataset from S3.

Presentations

Slides: https://github.com/rikturr/mml-feature-learning/blob/master/slides.pdf

Miami Machine Learning Meetup - 1/24/18

• Meetup: https://www.meetup.com/Miami-Machine-Learning-Meetup/events/246682464/

Fort Lauderdale Machine Learning Meetup - 2/21/18

• Meetup: https://www.meetup.com/Fort-Lauderdale-Machine-Learning-Meetup/events/247119469/
• Video: https://youtu.be/50S2jHbkO8U