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Executive Guide to Predictive Modeling Strategy at Scale

via LinkedIn Learning

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Overview

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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.

Syllabus

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

Taught by

Keith McCormick

Reviews

4.6 rating at LinkedIn Learning based on 450 ratings

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