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machine learning
Section 1
Introduction to Machine Learning
Basic Concepts of Machine Learning
Supervised Learning Overview
Unsupervised Learning Concepts
Section 2
Machine Learning Models
Linear Regression in-depth
Decision Trees and Random Forest Overview
Logistic Regression Fundamentals
Section 3
Neural Networks and Deep Learning
Deep Learning Concepts and Applications
Understanding CNNs
Neural Networks Basics
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Unit 2 • Chapter 1
Linear Regression in-depth
Summary
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Concept Check
What is a key assumption of linear regression?
Independence of residuals
Homoscedasticity of residuals
Normality of residuals
Linearity between independent and dependent variables
When is multicollinearity a problem in linear regression?
When residuals are normally distributed
When residuals have constant variance
When independent variables are highly correlated
When there are outliers in the data
What is the purpose of R-squared in linear regression?
To identify outliers in the data
To ensure the residuals are normally distributed
To detect multicollinearity in the independent variables
To measure the proportion of variance explained by the model
How does heteroscedasticity affect linear regression?
It increases the R-squared value in the model
It violates the assumption of constant variance of residuals
It improves the accuracy of the regression coefficients
It leads to multicollinearity issues
What is the difference between correlation and regression in statistics?
Correlation measures linear association, regression predicts values
Correlation measures cause-effect relationships
Regression assumes no relationship between variables
Regression measures strength of association, correlation predicts values
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Decision Trees and Random Forest Overview