A practical foundation for understanding Artificial Intelligence before moving into Machine Learning, Deep Learning, and applied AI engineering.
Understand what AI is, how intelligent agents work, search, heuristics, planning, decision making, reasoning, knowledge representation, and the basic idea of reinforcement learning.
Learn how to turn a real problem into states, actions, goals, constraints, and a search space that an AI system can work with.
Build the Python, NumPy, Pandas, probability, statistics, linear algebra, and basic calculus foundations needed for later Machine Learning.
The concepts are reinforced through small Python projects, including a rule-based agent, a state-space search solver, and exploratory data analysis.
The goal is not to memorize AI terms or blindly use frameworks. It is to understand the ideas well enough to explain them, implement simple systems, and build a strong foundation for Machine Learning and later AI engineering.