Machine learning practical

This hands-on course builds upon foundational machine learning concepts by focusing on practical implementation and real-world problem solving. Learners will apply data preprocessing, feature engineering, and model building techniques using popular ML tools and libraries such as Scikit-learn, TensorFlow, or similar. The course emphasizes supervised and unsupervised learning through projects involving classification, regression, and clustering. Students will also practice hyperparameter tuning, model evaluation, and performance optimization. By the end of the course, learners will have developed end-to-end ML pipelines and gained the skills to deploy models and interpret their results in a business or research context.