Linear Regression Basics

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 3 of 3

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Supervised Learning Fundamentals

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

Machine Learning Fundamentals

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

Linear Regression: Simple

Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.

Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Understanding Data Attributes

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

Untitled