Machine Learning Fundamentals (BTBS218)
The machine learning field covers a vast collection of automated methods that improve their own performance by learning patterns from data. This course provides students with a foundational understanding and programming competencies in machine learning, without requiring an extensive background in mathematics or statistics. It reinforces theoretical knowledge through practical applications. Students learn about the main categories of machine learning: supervised, unsupervised, and reinforcement learning. Topics include widely used algorithms such as Decision Trees, Support Vector Machines, Naive Bayes, and Clustering methods, as well as model evaluation techniques and preprocessing steps.