In this segment, you will see how ridge regression helps reduce the problem of overfitting using a simple dataset:

- First, we will build a linear regression model which can be considered as the ideal regression model which we want to obtain. Implementation of the linear regression model is shown in the forthcoming video.

- Now, we will fit an overfitting polynomial regression model on this linear data and observe the results.

- Eventually we will apply ridge regularisation to the polynomial regression model which we implemented earlier and see how it reduces model complexity to give us better results.

As you saw in the video, with increase in λ, the polynomial regression model becomes progressively simpler as the coefficients are pushed down towards zero. In the next segment, you will learn about another regularisation technique, i.e., Lasso regularisation.