Outline

• Clustering
• Regression
• Classification
• Dimensionality Reduction
• Recommendation systems

Clustering

http://scikit-learn.org/stable/modules/clustering.html#clustering

When to use it?

• When you want to find similar items
• Depends on your distance metric
• When you have to many items, and you want to aggregate

Examples

• Customer segmentation
• Grouping experiment outcomes

Regression

http://scikit-learn.org/stable/modules/linear_model.html#passive-aggressive-algorithms

When to use it?

• Present (Identify/Compare) Tendency
• Predict values

Linear Regression

Examples

• Stock prices
• Drug response

Classification

When to use it?

• Present (Identify/Compare) Tendency
• Present (Identify/Compare) Groups
• Aggregate
• Predict values

Examples

• Photo categorization
• Sentiment analysis
• Spam filtering

Dimensionality Reduction

When to use it?

• Attribute Filtering
• Categorize Documents (Topic modeling)

Recommendation systems

When to use it?

• Large catalog, with user preference history
• If you like a, and b, maybe you will like c

Types

• Collaborative filtering
• Content based systems
• Hybrids

Examples

• Amazon
• Facebook
• Google
• Yahoo
• Netflix Prize

How to use the algorithms?

• R
• scikit-learn
• Tensorflow (Google)
• Caffe
• Amazon ML
• Spark MLlib
• Graphlab

Applied example node + R