Linear models are a class of models that are widely used and extensively studied in the last few dacades, with roots going back over a hundred years. Linear Models can be used for both Classification & Regression problems.
Linear Regression is a linear approach to modelling the relationship between a scalar output (response) and one or more input variables (features).
We will go over the intuition and theoretical detail of the algorithm, apply it to both synthetic & real-world dataset to see exactly how it works,
and gain an intrinsic understanding of its inner-workings by writing code from scratch &
tuning its most important parameter alpha (regularization strength) to see its effect on model complexity and generalization.
Finally, we will explore the imporant parameters of the model, its strength & weakness.
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