Linear Models: Continued

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

Linear Models: Classification

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

Gradient Descent: Linear Regression

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Linear Classification Models: From Binary to Multiclass

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

Understanding Data Attributes

Covers the analysis of various data attributes and linear regression models.

Linear and Logistic Regression

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

Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Classification Algorithms: Generative and Discriminative Approaches

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

Machine Learning Review

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.