Udemy – Machine Supervised Learning: Regression in Python 3 and Math

Understand when to use simple, multiple, and hierarchical regression.,Effectively utilize regression models in your own work and be able to critically evaluate the work of others.,Make business decisions about the best models to maximize profits while minimizing risk.,Learn how to conduct correlation and regression.,Understand predicted values and their role in the overall quality of a regression model.

Artificial Intelligence has become prevalent recently. People across different disciplines are trying to apply AI to make their tasks a lot easier. For example, economists are using AI to predict future market prices to make a profit, doctors use AI to classify whether a tumor is malignant or benign, meteorologists use AI to predict the weather, HR recruiters use AI to check the resume of applicants to verify if the applicant meets the minimum criteria for the job, etcetera. The impetus behind such ubiquitous use of AI is machine learning algorithms. For anyone who wants to learn ML algorithms but hasn’t gotten their feet wet yet, you are at the right place. The rudimental algorithm that every Machine Learning enthusiast starts with is a linear regression algorithm. Therefore, we shall do the same as it provides a base for us to build on and learn other ML algorithms.

Before knowing what is linear regression, let us get ourselves accustomed to regression. Regression is a method of modeling a target value based on independent predictors. This method is mostly used for forecasting and finding out the cause and effect relationship between variables. Regression techniques mostly differ based on the number of independent variables and the type of relationship between the independent and dependent variables.

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