Machine Learning Course Description
This Machine Learning course provides a comprehensive introduction to the concepts, techniques, and tools used to build intelligent systems. Students will learn how computers can automatically improve their performance by analyzing data, identifying patterns, and making informed decisions with minimal human intervention.
The course covers key ML approaches, including supervised learning, unsupervised learning, and reinforcement learning. Learners will work with essential algorithms such as linear regression, logistic regression, decision trees, clustering, neural networks, and more. Alongside theoretical understanding, the course emphasizes hands-on experience through practical projects using popular tools like Python, NumPy, Pandas, Scikit-learn, and TensorFlow.