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Related lectures (29)

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Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.

Support Vector Machines: Soft Margin

Explores Support Vector Machines with a focus on soft margin and multiclass classification using binary classifiers.

Linear Classification: Logistic Regression

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

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Multi-Class Classification: Approaches and Boundaries

Explains the strategies for multi-class classification and the concept of decision boundaries.

Generalized Linear Regression

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

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Recommender Systems: Text Classification & Naïve Bayes

Explores text classification using Naïve Bayes in content-based recommenders.

Linear Models for Classification: Multi-Class Extensions

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

Classification Problems: Overview and Loss Functions

Covers classification problems and various loss functions used in machine learning.