Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Feature Scaling

The video tutorial explains feature scaling, also known as normalization, and its significance in machine learning. It covers the steps of centering and scaling data, highlighting the importance of scaling for optimization algorithms and model performance. The tutorial also discusses batch normalization in neural networks, emphasizing its benefits in improving training time and addressing issues like exploding or vanishing gradients. The video concludes with best practices for feature scaling, encouraging its use in data science models.

What are the consequences of not scaling features with different units?

How does feature scaling affect the convergence of optimization algorithms?

What is batch normalization and how does it relate to feature scaling?