Slow algorithms: More calculations

Scaling machine learning initiatives

Defining terms

Data and supervised machine learning

The nine big data bottlenecks

The stages of predictive analytics data

Why you might have too little data

How much data do I need?

Balancing

Who truly has big data?

Assessing data

Selecting: Data that should be left out

Seasonality and time alignment

Data and the data scientist

Aggregate and restructure

Dummy coding

Feature engineering

Understanding the modeling process

Slow algorithms: Brute force

Slow algorithms: More models

How to sample properly

Modeling with missing data

Looking ahead to deployment and scoring in production

Continuing your predictive modeling journey