Linear Regression Explained | Machine Learning for Beginners

Linear Regression is one of the most important algorithms in Machine Learning and serves as the foundation for understanding supervised learning, optimization, and statistical modeling. It is widely used for predicting continuous values such as house prices, sales forecasts, and business analytics. In this video, you'll learn: ✅ What Linear Regression is ✅ Understanding Supervised Learning ✅ Predicting house prices using Linear Regression ✅ Hypothesis Function explained ✅ Least Squares Cost Function ✅ Batch Gradient Descent step by step ✅ Stochastic Gradient Descent (SGD) explained ✅ Comparing Batch GD vs SGD ✅ The Normal Equation for direct parameter estimation ✅ Matrix derivatives made simple ✅ Maximum Likelihood Estimation (MLE) explained ✅ Gaussian Noise assumption ✅ Likelihood vs Probability explained ✅ Real-world applications of Linear Regression Whether you're a Machine Learning Engineer, Data Scientist, AI Student, Software Developer, or anyone starting their AI journey, this video provides a complete understanding of one of the most essential machine learning algorithms. Topics Covered: • Linear Regression • Supervised Learning • Machine Learning • Gradient Descent • Batch Gradient Descent • Stochastic Gradient Descent • Normal Equation • Maximum Likelihood Estimation (MLE) • Least Squares • Gaussian Distribution • Artificial Intelligence • Data Science Discover why Linear Regression remains one of the most important algorithms in AI and how its mathematical foundations influence many advanced machine learning models. 🔔 Subscribe for more videos on Machine Learning, Deep Learning, Data Science, AI Engineering, Statistics, Mathematics for AI, and Generative AI. #LinearRegression #MachineLearning #ArtificialIntelligence #GradientDescent #SupervisedLearning #DataScience #MaximumLikelihood #LeastSquares #Statistics #AIEngineering #DeepLearning #MLTutorial #Mathematics #PredictiveAnalytics #GenerativeAI Timestamps: 00:00 Introduction 01:40 What is Linear Regression? 05:10 Supervised Learning Explained 09:30 House Price Prediction Example 14:15 Hypothesis Function 19:20 Least Squares Cost Function 24:30 Batch Gradient Descent 30:10 Stochastic Gradient Descent 35:20 Normal Equation 41:10 Maximum Likelihood Estimation 46:20 Likelihood vs Probability 51:00 Key Takeaways