MLA One Shot #2 | Linear Regression & Multivariate Regression | MSE, Gradient Descent | MAKAUT AIML

🚀 MLA (Machine Learning Applications) One Shot #2 for MAKAUT CSE (AI & ML) students. In this lecture, we cover Linear Regression from complete intuition to mathematical formulation, along with Multivariate Linear Regression, Cost Function (MSE), and Gradient Descent. This session is designed specifically for semester exam preparation and follows the MAKAUT syllabus. 📚 Notes Sample: https://drive.google.com/drive/folder... 📩 Contact on Telegram: https://t.me/+Ceqr3SrUSWQ4MzA1 📝 Purchase Complete Notes: https://forms.gle/FjDf1t3Jvyuqd4d6A 📚 Topics Covered: ✔️ What is Regression? ✔️ Linear Regression Intuition ✔️ Best Fit Line Concept ✔️ Hypothesis Function ✔️ Cost Function (Mean Squared Error - MSE) ✔️ Gradient Descent Algorithm ✔️ Learning Rate & Convergence ✔️ Multivariate Linear Regression ✔️ Feature Representation ✔️ Numerical Examples ✔️ Important Exam Questions & Concepts 🎯 Perfect for: MAKAUT CSE (AI & ML) Last-minute semester preparation University exams Viva preparation Quick revision before exams 👍 If this lecture helps you, don't forget to Like, Share, and Subscribe. It motivates me to create more free exam-oriented content for everyone. Timestamps: 00:00 Introduction to Linear Regression 02:08 Simple Linear Regression 06:56 Cost Function (Mean Squared Error) 10:08 Methods to Find Minima 10:44 Closed Form Solution (OLS) 15:36 Gradient Descent Algorithm 24:09 Variants of Gradient Descent 26:45 Assumptions of Linear Regression & Evaluation Metrics 33:19 Multivariate Linear Regression 41:33 Normal Equation, Feature Scaling & Polynomial Regression #MLA #MachineLearningApplications #LinearRegression #GradientDescent #MAKAUT #AIML #MachineLearning #MultivariateLinearRegression #MSE #ExamPreparation #OneShot #AnmolKansal