Capella University 6 credits CSC
Students investigate modern techniques and workflows for training, testing, and applying machine learning models in this introductory course. Students gain an understanding of industry standard ML frameworks, including TensorFlow, PyTorch, and Keras. Students explore foundational training methodologies such as supervised, unsupervised, and reinforcement learning; neural and deep-neural networks; and clustering and ensemble methods. Students utilize open-source image and structured data sets to evaluate the effects of over-fitting and generalization on model performance. Prerequisite(s): IT2249; MAT1200 or MAT2200.
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