Unsupervised Machine Learning with Python

Description Unsupervised Machine Learning involves finding patterns in datasets. After taking this course, students will be able to understand, implement in Python, and apply algorithms of Unsupervised Machine Learning to real-world datasets. The core of this course involves detailed study of the following algorithms: Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model Dimension Reduction: Principal Component Analysis The course presents the math underlying these algorithms including normal distributions, expectation maximization, and singular value decomposition. The course also presents detailed explanation of code design and implementation in Python, including use of vectorization for speed up, and metrics for measuring quality of clustering and dimension reduction. The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).