Logistic Regression: Part 1

Related lectures (31)

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Covers linear classification using logistic regression, regularization, and multiclass classification.

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Covers supervised learning, classification using logistic regression, and challenges in optimization.

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

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.