Machine Learning Basics

Related lectures (32)

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

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

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

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

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

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

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.