Machine Learning Basics: Supervised and Unsupervised Learning

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

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

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, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

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

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

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

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

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

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