Introduction to Multiple Linear Regression | AIML End-to-End Session 75

Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?

Welcome to Session 75 of the AIML End-to-End series, where we dive into Multiple Linear Regression, an essential machine learning technique for modeling relationships between multiple input variables and a target output. In this session, you'll learn how to build, train, and evaluate multiple linear regression models, understand the role of each variable, and interpret the results using Python libraries like Scikit-learn. We’ll also cover key concepts such as multicollinearity, p-values, and adjusted R-squared, and discuss best practices for feature selection and model improvement.

By the end of this session, you’ll have a strong foundation in applying multiple linear regression to solve real-world predictive modeling problems.

Key Topics:

Understanding Multiple Linear Regression
Building and evaluating models with multiple predictors
Addressing multicollinearity and feature selection



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#MultipleLinearRegression #MachineLearning #AIML #EndToEnd #DataScience #PredictiveModeling #Multicollinearity #FeatureSelection #ScikitLearn #Python #aiml


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