This course, Understanding AI from Scratch, is a practical introduction to neural networks and artificial intelligence without relying on external machine learning libraries. It is designed to help learners deeply understand how AI systems work by building them step by step from the ground up.

The course focuses on constructing a neural network manually, including defining neurons, adjusting weights, and understanding how inputs are transformed into outputs. Instead of treating AI as a black box, learners explore the internal mechanics of how models learn and make decisions.

A key feature of the course is a self-driving car simulation, where learners train an AI agent in a virtual environment. By manually tuning parameters and observing behavior changes, students gain hands-on experience with how learning systems evolve over time.

The course also introduces important concepts such as genetic algorithms, hidden layers, decision-making systems, and shortest-path reasoning. These ideas help bridge the gap between theory and real-world AI behavior.

By the end of the course, learners develop a strong foundational understanding of neural networks and AI logic. It is ideal for anyone who wants to move beyond high-level frameworks and truly understand how artificial intelligence works at its core.