This video is an advocacy for linear models. Its goal is to convince you that they should always be your first choice. Especially, if you care about model interpretability. This is because they are easier to explain, widely understood and accepted in many industries. Building them also requires you to think more critically about your problem and data. Most importantly, a well-structured linear model will often match the performance of more complex models.

Now, that last one may be a bold statement. It does take some hard work to make it true. We will see how by comparing the ability of logistic regression and a deep neural network to model a non-linear decision boundary. We will see that, even though logistic regression is a linear model, we can use feature engineering to model non-linear relationships.

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🚀 Chapters 🚀
00:00 Introduction
01:53 Non-linear decision boundary
03:41 "Dumb" logistic regression
05:25 Deep neural network
06:26 Feature engineering