Machine Learning

This course provides a comprehensive introduction to machine learning, covering both theoretical foundations and practical applications. Students will learn about various machine learning models, including supervised and unsupervised learning techniques such as linear regression, decision trees, support vector machines, clustering, and neural networks. The course also explores key concepts like model evaluation, overfitting, regularization, and hyperparameter tuning. Through hands-on projects and coding exercises, students will gain experience in implementing machine learning algorithms using popular frameworks and libraries such as TensorFlow, Keras, and Scikit-Learn. By the end of the course, students will be equipped with the skills to develop and deploy machine learning models to solve real-world problems. A strong foundation in programming and basic statistics is recommended.