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In simple words, bias means how far you have come in predicting the desired value from your actual value. It is an approach that can ultimately make or break the model in favor or against your idea. A straightforward example can be: When we talk about the famous linear regression model, we quantify the relationship between X and Y variable as linear; on the contrary, in reality, the relationship might not be perfectly linear as we had read. Variance is the reverse of bias. It is called the variance when your model performs exceptionally well on the training dataset yet fails to live up to the same standards when running it on an entirely new dataset. In simple words, your model conveys to you that the predicted values are very scattered from the actual values. This concept is similar to the overfitting of the model on the dataset, also called the difference between the model fits when used on different datasets.

01:25 - Agenda
01:56 - Introduction
04:35 - Bias and Variance in Machine Learning
07:42 - Difference between Bias and Variance
08:15 - Bias vs Variance
13:14 - Bias Variance Trade-Off
18:03 - Bias and Variance In Machine Learning
18:34 - Total Error
21:02 - Hands-On
27:00 - Summary

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