Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here I go through two examples that make these concepts super easy to understand.
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0:00 Awesome song and introduction
0:29 The data and the "true" model
1:23 Splitting the data into training and testing sets
1:40 Least Regression fit to the training data
2:16 Definition of Bias
2:33 Squiggly Line fit to the training data
3:40 Model performance with the testing dataset
4:06 Definition of Variance
5:10 Definition of Overfit
Correction:
4:06 I say that the difference in fits between the training dataset and the testing dataset is called Variance. However, I should have said that the difference is a _consequence_ of variance. Technically, variance refers to the amount by which the predictions would change if we fit the model to a different training data set.