This course, created by Radu Mariescu-Istodor and featured on freeCodeCamp, provides a deeply practical introduction to artificial intelligence and neural networks by building models completely from scratch without using machine learning libraries.

The course focuses on helping learners understand how neural networks actually work at a fundamental level. Instead of relying on pre-built frameworks, students manually implement and adjust parameters to train a simple AI system. One of the key projects involves teaching a simulated car how to drive inside a custom-built environment.

Throughout the lessons, learners explore essential concepts such as neurons, inputs, outputs, and hidden layers. The course also introduces genetic algorithms and demonstrates how AI can evolve behavior over time through trial and error.

As the course progresses, more advanced ideas are introduced, including decision-making in traffic systems, sensor-based inputs, and pathfinding algorithms like Dijkstra’s algorithm. These concepts help learners understand how AI systems interact with dynamic environments.

The course emphasizes intuition and hands-on learning, making complex ideas more accessible by breaking them into simple, visual experiments. By building everything from scratch, learners gain a deep understanding of how neural networks function internally rather than treating them as black boxes.

By the end, students develop strong foundational knowledge in AI, neural networks, and algorithmic thinking, making this course ideal for anyone who wants to master machine learning from the ground up.