Lecture
Linear Regression Basics
Related lectures (48)
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Linear Regression: Introduction and Training
Covers linear regression training to find the best line for given data points, essential for predicting house prices.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Linear Models: Basics
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Linear Regression: Basics
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
Machine Learning Basics
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Introduction to Machine Learning
Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.