Machine Learning Tutorial Chap 5| Part-2 L1 Regularization

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Here is What You'll Learn

Understand the various problems of Linear Regression

Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable).

Understand Regularization and its types

This is a form of regression, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. A simple relation for linear regression looks like this.

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.

Learn about ways to handle Non-Linear Data

Data structures where data elements are not arranged sequentially or linearly are called non-linear data structures.

Distinguish between L1 and L2