Applied Machine Learning
Term 2, Winter Session 2019-2020
Application of machine learning tools, with an emphasis on solving practical problems. Data cleaning, feature extraction, supervised and unsupervised machine learning, reproducible workflows, and communicating results.
Weekly pair assignments used Python and Jupyter Notebooks. We explored several datasets found on Kaggle in order to gain insight into real problems that could be solved with machine learning.
We also used many scikit-learn tools to evaluate the effectiveness of different models of classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means.
Another important aspect of the class was the focus on best practices when preprocessing, training, and evaluating our models.