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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 3
Logistic Regression Fundamentals
Summary
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Concept Check
What is not a common assumption of Logistic Regression?
Independence of observations
Normal distribution of errors
Linearity of independent variables
Homoscedasticity
What type of data is Logistic Regression suitable for?
Continuous data
Categorical data
Binary or dichotomous data
Ordinal data
What is an appropriate evaluation metric for Logistic Regression?
Mean Squared Error (MSE)
Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
Adjusted R-squared
R-squared
Which method is used for parameter estimation in Logistic Regression?
Maximum Likelihood Estimation (MLE)
Ordinary Least Squares (OLS)
Principal Component Analysis (PCA)
K-means Clustering
What transformation function is used in Logistic Regression?
Logistic or Sigmoid function
Linear function
Exponential function
Polynomial function
Check Correctness
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Decision Trees and Random Forest Overview