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Naive Bayes: Generative Learning Algorithms
Machine Learning
Lecture 29 of 30
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Welcome
What is Machine Learning?
Supervised Learning Introduction
Unsupervised Learning Introduction
Installing Octave
Supervised Learning Introduction
Model Representation
Cost Function
Gradient Descent
Gradient Descent for Linear Regression
Vectorized Implementation
Feature Scaling
Learning Rate
Features and Polynomial Regression
Normal Equations
Classification
Model
Optimization Objective I
Optimization Objective II
Gradient Descent
Newton's Method I
Newton's Method II
Gradient Descent vs Newton's Method
The Problem Of Overfitting
Optimization Objective
Common Variations
Regularized Linear Regression
Regularized Logistic Regression
Generative Learning Algorithms
Text Classification
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