This Artificial Intelligence & Machine Learning course provides a beginner-friendly yet comprehensive introduction to the core concepts and practical techniques of AI. The journey begins with foundational knowledge—understanding what AI is, how search spaces work, and implementing basic search algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS). As the course progresses, students dive into heuristic-based approaches such as A*, followed by building rule-based systems for intelligent decision-making.

The course then shifts toward probabilistic reasoning through Bayes’ Theorem and introduces basic machine learning techniques like linear regression and the perceptron. Students are guided through neural network fundamentals, using Keras to build and train simple models. Key challenges such as overfitting and regularization are addressed to help develop reliable AI systems.

Alongside technical depth, the course incorporates critical discussions on AI ethics, bias, and fairness—ensuring learners are equipped to build responsible AI. Students explore the foundations of Natural Language Processing (NLP), including tokenization and building a simple rule-based chatbot. Evaluation metrics are introduced to assess model performance effectively.

The latter part of the course is hands-on and project-driven, starting with a capstone proposal and guiding students through dataset preparation, model training and tuning, and performance visualization. Special focus is placed on identifying and mitigating bias, ensuring fairness in outputs. Finally, students prepare a user guide and present a working AI project in the capstone demo.

By the end of the course, students will have both the theoretical understanding and practical skills to build, evaluate, and present basic AI models confidently—laying a strong foundation for further exploration in the field.