Logistic Regression: Fundamentals and Applications

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Page 3 of 4

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Linear Models for Classification

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

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.

Linear and Logistic Regression

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Linear Models: Continued

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

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: Classification

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