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Part 1: Introduction to the Course & Python
Week(s)Topics
1 to 4General Introduction to Machine Learning & core Python constructs/concepts (variables, conditions, statements, iteration, functions, lists, tuples, dictionaries, & general i/o)


Part 2: Specialized Python Modules & Packages
Week(s)Topics
5 to 7Pandas, Numpy, ResearchPy, Visualization (Matplotlib, Seaborn, Bokeh) (time permitting), & others (time permitting)


Course Phase Milestone

Part 3: Supervised Machine Learning
Week(s)Topics
8 to 13Machine Learning Terminology, K-Nearest Neighbors, SGD Classifier & Regressor (Gradient Descent), Decision Trees, Random Forests, Support Vector Machines, Boosted Trees & Forests (time permitting), & Keras Neural Networks (time permitting).


Part 4: Unsupervised Machine Learning
Week(s)Topics
14 to 15K-Means & Principal Component Analysis.


“We cannot direct the wind, but we can adjust the sails.” --Dolly Parton


Course Objectives