ML03_ Linear Regression in Machine Learning: Concepts, Mathematics, Gradient Descent

This lecture provides a clear and rigorous introduction to Linear Regression, one of the foundational models in machine learning. Designed for learners in AI, data science, and applied mathematics, the session covers both the theoretical underpinnings and practical considerations of building, evaluating, and improving regression models. You will explore the definition and intuition behind linear regression, its key application domains, real-world use cases, and the criteria for selecting regression models. The lecture introduces the mathematical formulation, types of errors, model evaluation metrics, gradient descent optimization, and challenges such as overfitting. Techniques like L1/L2 regularization and extensions to multivariable regression are also discussed. This lesson aligns with modern AI and data science practices and supports a deep conceptual understanding for advanced study and research. ✅ Hashtags #MachineLearning #LinearRegression #AI #DataScience #GradientDescent #ModelEvaluation #Regularization #DeepLearning #MLAlgorithms #Statistics #SupervisedLearning #PredictiveModeling