This course provides a comprehensive introduction to machine learning concepts, focusing on supervised and unsupervised learning. Students will learn how to train models using labeled data to make predictions (supervised learning) and how to uncover patterns and hidden structures in unlabeled data (unsupervised learning). The course includes practical examples like image classification, prediction, data clustering, and dimensionality reduction, equipping students with the skills to apply these techniques in various fields.

This course introduces students to the fundamentals of neural networks, the building blocks of deep learning. It covers topics such as perceptron, activation functions, feedforward and backpropagation algorithms, and network optimization. Students will learn to design, train, and evaluate neural networks for tasks like image recognition, natural language processing, and predictive analytics. The course includes hands-on projects to provide practical experience in building neural network models using frameworks like TensorFlow or PyTorch. 

This course introduces the principles of reinforcement learning, where agents learn to make decisions by interacting with their environment to maximize rewards. Key topics include Markov decision processes, Q-learning, policy optimization, and deep reinforcement learning. Students will explore applications in robotics, game playing, and autonomous systems through hands-on projects using frameworks like OpenAI Gym and TensorFlow. By the end, they will gain the skills to design and train intelligent agents for real-world scenarios.