Machine Learning and AI Foundations: Predictive Modeling Strategy at Scale

Scalability is one of the biggest challenges in data science. Learn how to evaluate data, choose the right algorithms, and perform predictive modeling at scale.

Introduction

• Scaling machine learning initiatives
• Defining terms

1. The Phases of a Machine Learning Project

• Data and supervised machine learning
• The nine big data bottlenecks
• The stages of predictive analytics data
• Why you might have too little data

2. Designing a Machine Learning Dataset

• 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

3. Data Prep Challenges

• Data and the data scientist
• Aggregate and restructure
• Dummy coding
• Feature engineering

4. Chapter Name

• Understanding the modeling process
• Slow algorithms: Brute force
• Slow algorithms: More calculations
• Slow algorithms: More models
• How to sample properly
• Modeling with missing data
• Looking ahead to deployment and scoring in production

Conclusion

• Continuing your predictive modeling journey