Predictive Models with Machine Learning

This intermediate-level course explores machine learning fundamentals with a focus on developing predictive, regression, and classification models using Python. Students will learn how data science is supported by machine learning techniques, which fall into two main categories: Supervised and Unsupervised Learning. The four-module curriculum begins with an introduction to data modeling, where students install necessary software and create simple linear regression models to understand machine learning's potential. The second module covers regression and classification methods, teaching students to define mathematical models for prediction, analysis, and pattern identification to support decision-making. In the third module, students learn variable selection techniques and data preparation methods to optimize prediction and control models using various Python-based machine learning approaches. The final module explores optimization techniques for complex models, including clustering algorithms and time series analysis for more precise predictions. Throughout the course, students will recognize machine learning's applications in robotics while building practical skills in model construction, optimization, and implementation. By course completion, participants will be able to create effective predictive models, apply optimization techniques, and implement machine learning solutions for real-world problems.