The Machine Learning (ML) Stream exposes students to a comprehensive, project-based flow of courses starting with introductory, conceptual-based topics in classification, object detection and neural networks. The Stream then provides a deep-dive into Python fundamentals, data structures, object-oriented programming, recursion, and relevant applications to Machine Learning and Data Mining. Next, the Stream provides a foundation of the Python specific libraries used in Machine Learning in addition to a Statistics primer course, which includes many of the topics covered in a high school level advanced-placement Statistics class. The students are further presented with Data Mining techniques and introduced to regression algorithms and databases. Next, students are taught the traditional Machine Learning algorithms used in practice today, including supervised and unsupervised classification. Next, several courses providing a deep-dive into neural network construction, training, and analysis are then presented. Finally, students are exposed to advanced topics and applications in Machine Learning.
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ML-100 Intro to Machine Learning Classifiers - Part 1 (ML-100)
ML-105 Intro to Machine Learning Classifiers - Part 2 (ML-105)
ML-110 Object Detection Overview (ML-110)
ML-205 Python Data Structures and Advanced Functions (ML-205)
ML-220 Python Object Oriented Programming and Recursion (ML-220)
ML-230 Python File Input/Output and Exception Handling (ML-230)
ML-300 Data Mining Intro and Preprocessing (ML-300)
ML-310 Statistics Primer (ML-310)
