Supervised Learning

Historical data predicts likely future events, via methods such as classification, regression, prediction and gradient boosting.

Semi Supervised Learning

The cost associated with labeling is too high to allow for a fully labeled training process.

Unsupervised Learning

The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data.

Reinforcement Learning

Used for robotics, gaming and navigation. The algorithm discovers through trial and error which actions yield the greatest rewards.

Automotive

Automotive

Education

Education

Energy

Energy

Financial-Services

Financial Services

Gaming

Gaming

Governments

Governments

Healthcare

Healthcare

Retail

Retail