<ul data-eligibleForWebStory="false"><li>Linear Regression is a supervised learning algorithm used for predicting a continuous target variable based on input features with a linear relationship assumption.</li><li>Types of Linear Regression include simple linear regression, multiple linear regression, as well as variations like Ridge, Lasso, ElasticNet, and Polynomial Regression.</li><li>The goal of linear regression is to minimize the cost function, commonly using Mean Squared Error (MSE), by adjusting model parameters through techniques like gradient descent.</li><li>The article provides insights into the mathematical representation, types, cost function, gradient descent, and Python code implementation for Linear Regression, with a promise of upcoming advanced topics in machine learning.</li></ul>

Week 7: Machine Learning Algorithms-Linear Regression: Part 1

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