Machine Learning 02 | Linear Regression | DA | GATE Crash Course

This session covers both simple and multiple linear regression techniques, exploring concepts like hypothesis functions, least squares fit, cost function optimization, and gradient descent algorithms. Understand how linear regression models relate input features to continuous outputs, with detailed derivations, real-world examples, and step-by-step problem-solving aligned with the GATE Data Science syllabus. Mastering these concepts is crucial for scoring high in the Machine Learning section and building a strong foundation for advanced ML topics in GATE 2026.

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Timestamps:
00:00:00 OLS Method
01:04:44 Multiple Linear Regression

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