Linear Models: Recap and Logistic Regression

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Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Logistic Regression: Cost Functions & Optimization

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Logistic Regression: Classification

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

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Logistic Regression: Vegetation Prediction

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

Linear Models: Part 2

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.