I. Random Forests and SVM

The Random Forests and SVM course aims to provide learners with an understanding and the ability to apply two key machine learning techniques: Random Forests and Support Vector Machine (SVM) for data analysis. It begins with an explanation of the fundamentals of each technique, the underlying workings, and their applications in various scenarios. Additionally, the course covers how to evaluate model performance using different methods, such as assessing feature importance and fine-tuning parameters for optimization.

Learners will engage in demonstrations that encompass the creation and refinement of models, utilizing Python libraries for data processing and model building, understanding results from models, and enhancing model performance. The course also includes assessments and real-world projects for learners to practice and strengthen their skills in data analysis and the application of machine learning techniques in practical situations.