Logistic Regression: Part 1

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Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

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Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Linear Models: Classification

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

Generalized Linear Regression

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

Linear Models for Classification: Part 3

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

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Linear Classification Models: From Binary to Multiclass

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Logistic Regression: Fundamentals and Applications

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

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.