Linear Regression Basics

Related lectures (29)

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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers linear regression training to find the best line for given data points, essential for predicting house prices.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.