Unsupervised Machine Learning with Python

After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets. Unsupervised Machine Learning involves finding patterns in datasets. Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition. 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). All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.