A Machine Learning course introduces the concepts and techniques used to enable computers to learn from and make predictions or decisions based on data. It covers fundamental topics such as supervised and unsupervised learning, neural networks, deep learning, and model evaluation. Students learn how to implement algorithms, handle various data types, and apply statistical methods to improve model performance. The course often includes practical exercises using programming languages like Python and tools like TensorFlow or scikit-learn. By the end, learners should be able to design, train, and deploy machine learning models to solve real-world problems.