Regression Trees and Ensemble Methods in Machine Learning

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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Introduction to Machine Learning: Linear Models

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

Linear Regression: Foundations and Applications

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Machine Learning Fundamentals

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

Model Evaluation

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Linear Models: Part 1

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

Cross-Validation: Techniques and Applications

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

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.