Overfitting vs Underfitting & Bias in ML Explained | AI Explained #6

Overfitting vs Underfitting & Bias in ML Explained – episode 6 of AI Concepts Explained, a free beginner-friendly AI and machine learning course by Irfan Hayat. Why do machine learning models fail? Irfan Hayat explains underfitting, overfitting, the bias-variance trade-off and data bias with animated charts and a Python polynomial example, plus practical fixes like validation, regularization and better data. What you'll learn • Underfitting vs overfitting with a simple chart • The bias-variance trade-off • Seeing overfitting in Python • Fixes: more data, regularization, validation, early stopping • Data bias and why fairness matters Chapters 00:00 Intro 00:39 Underfitting vs overfitting 01:22 Overfitting in Python 02:05 Bias-variance trade-off 02:44 How to fix it 03:29 Data bias and fairness 04:14 Regularization in Python 05:17 Train, validation and test splits 06:09 Early stopping in practice 07:04 Quick quiz 08:02 Recap 08:36 Next: Computer vision All slides, code examples and explanations are original. Narration uses a synthetic (text-to-speech) voice reading the instructor's original script. No music, third-party footage or logos are used. ▶ Full series: AI Concepts Explained playlist #MachineLearning #ArtificialIntelligence #DataScience