Supervised Learning - Logisitic Regression
What we will learn here
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.
Logisitic Regression is similar to linear regression, but instead of predicting a continuous value, it predicts which class (or) category the input data belongs to.
We will go over the intuition 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
C (inverse of regularization strength) to see its effect on model complexity and generalization.
Finally, we will explore the imporant parameters of the model, its strength & weakness.
