In Episode 22, we explored training data—the foundation of machine learning.
In Episode 23, we transformed that data into meaningful signals through feature engineering.

Now in Episode 24, we take the next step:

How does AI actually learn from those signals?

In this episode of AI Made Simple, we break down modeling and training—the core of how machine learning systems work. We explain how models learn patterns, what loss functions are, and why concepts like overfitting and generalization are critical in real-world systems.

We also cover:

• The learning loop and how models improve
• Loss functions and optimization (simplified)
• Overfitting vs generalization
• Types of models and real-world trade-offs
• Training pipelines and continuous learning

This is Part 3 of the series. Next, we’ll cover experimentation—how companies evaluate models and decide what actually works.