Students get knowledge about applying machine learning models in practical situations in addition to model training. Numerous machine learning algorithms are covered throughout the lessons, such as reinforcement learning, supervised learning, and unsupervised learning. Students are equipped with practical abilities in feature engineering and data exploration, enabling them to efficiently prepare data for machine learning assignments. To improve model performance, lessons cover model optimization and hyperparameter tuning strategies. Pupils acquire knowledge about enhancing the effectiveness and precision of machine learning models. Tests are how I assess a student's development.