*Linear Regression (part-1)* | Industry Relevant AI/ML Course

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Whether you're a student, working professional, developer, or complete beginner, this detailed module will build a strong foundation for the rest of the course.

In this video, you will learn Linear Regression from fundamentals to interview level. We explain the statistical meaning of regression, independent vs dependent variables, expected value, and why real-world data contains noise and errors.

You’ll understand supervised learning setup, weights and bias, loss function (MSE), closed-form solution, why it fails in practice, and why Gradient Descent is widely used.

We cover all key Linear Regression assumptions: linearity, independence of errors, homoscedasticity, normality of residuals, and multicollinearity—explained with clear intuition and real-world examples.

Finally, we discuss regression evaluation metrics: MSE, RMSE, MAE, R², and Adjusted R², including why high R² does not guarantee generalization.

By the end, you’ll know how to clean data the right way and build leakage-free ML pipelines that generalize well.

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Видео *Linear Regression (part-1)* | Industry Relevant AI/ML Course канала Sonu Yadav AIML [IIT-DELHI]