First, we're going to go over the analytical view to give a logical review of how we're achieving our goal of reducing complexity. We're going to go through a few approaches to adding intuition to this process. The goal here is to shed additional light so that regularization approaches don't seem as much of a black box. Here we'll examine intuitively how regularization works. In this section, we'll be covering the details of regularization and some different approaches to understanding exactly how regularization works. In this video, we're going to go into deeper detail in regards to regularization to gain a deeper intuitive understanding of how it works. To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics. This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. Use regularization regressions: Ridge, LASSO, and Elastic net Use a variety of error metrics to compare and select a linear regression model that best suits your dataĪrticulate why regularization may help prevent overfitting This course also walks you through best practices, including train and test splits, and regularization techniques.īy the end of this course you should be able to:ĭifferentiate uses and applications of classification and regression in the context of supervised machine learningĭescribe and use linear regression models
WHAT AFTER COURSERA MACHINE LEARNING HOW TO
You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression.
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