About This Course

Machine Learning is a subfield of artificial intelligence that enables systems to learn from data and make decisions with minimal human intervention. This course provides a solid foundation in the concepts and techniques used in modern machine learning, covering both theoretical principles and practical applications.

Throughout the course, learners will explore key ML paradigms such as supervised learning, unsupervised learning, and reinforcement learning. Topics include regression, classification, clustering, decision trees, support vector machines, neural networks, and model evaluation metrics. Emphasis is placed on understanding when and how to apply each technique to real-world problems.