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Machine Learning (coursera)
Contents
1
Overview
2
Prediction: Supervised Learning
2.1
Linear Regression
3
Classification
3.1
Logistic Regression
3.2
Neural Networks
3.3
Support Vector Machines
4
Unsupervised Learning
5
Other
5.1
Model Debugging
5.2
Error Metrics
5.3
Practical Advice
Overview
Introduction to
Machine Learning
Octave tutorial @ Coursera crowd wiki
Prediction:
Supervised Learning
Linear Regression
Univariate
Linear Regression
Gradient Descent
Multivariate Linear Regression
Gradient Descent for Multivariate Linear Regression
Normal Equation
Classification
Logistic Regression
One-vs-All Classification
Overfitting
and
Regularization
Neural Networks
Representation
Forward Propagation
Back Propagation
Support Vector Machines
Intuition behind SVM
Unsupervised Learning
Clustering
K-Means
Dimensionality Reduction
Anomaly Detection
Recommender Systems
Other
Model Debugging
Machine Learning Diagnosis
Learning Curves
Model Selection
Cross-Validation
Error Analysis
- also good for prioritizing
Error Metrics
Precision
Recall
$F_1$-score
Practical Advice
Large-Scale Machine Learning
Photo OCR Application Example (Machine Learning)
Categories
:
Machine Learning
Coursera
Notes