Course description

Machine Learning is a subset of Artificial Intelligence that enables computers to learn from data and improve their performance without being explicitly programmed. It forms the foundation of modern technologies such as chatbots, recommendation systems, image recognition, and Generative AI tools like ChatGPT. Machine learning techniques are broadly divided into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training models on labeled datasets to perform tasks such as classification and regression, including spam detection and price prediction. Unsupervised learning works with unlabeled data to discover hidden patterns through clustering and dimensionality reduction. Reinforcement learning trains agents to make decisions using rewards and penalties and is widely used in robotics, automation, and game AI. Understanding these core concepts helps learners build intelligent applications, fine-tune large language models, develop real-world AI products, and confidently transition into Generative AI development. Mastering machine learning fundamentals ensures stronger problem-solving skills and prepares students for careers as AI/ML engineers, GenAI developers, and prompt engineers.

What will i learn?