Machine Learning Fundamentals

This course is only available to students who have been accepted into the Foundational Data Science Advanced Certificate.

(Pre-Requisite Courses: DSC-207R and DSC-215R)

This online course covers the fundamentals of supervised and unsupervised learning algorithms, and the theory behind those algorithms. Application of techniques utilizing Python and Jupyter notebooks through real-world case studies. Classification, regression, and conditional probability estimation; Generative and discriminative models; Linear models and extensions to nonlinearity using kernel methods; Ensemble methods: boosting, bagging, random forests; Representation learning: clustering, dimensionality reduction, autoencoders, deep neural networks.