Applied Machine Learning

The course introduces students to the principles and techniques of machine learning and their practical applications. The course covers topics such as data preprocessing, feature engineering, model selection, and evaluation. Students will learn how to implement algorithms for supervised and unsupervised learning, and how to interpret and visualize the results. They will also gain hands-on experience with popular machine learning tools and libraries such as scikit-learn and TensorFlow. The course will equip students with the knowledge and skills necessary to develop and apply machine learning solutions in real-world scenarios.