Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

Machine Learning and AI Foundations: Predictive Modeling Strategy at Scale

via LinkedIn Learning

Go to class Write review
Udacity Ad

Master AI & Data—50% Off Udacity (Code CC50)

Learn More → TrainSec Ad

Master Windows Internals - Kernel Programming, Debugging & Architecture

Learn More →

Overview

Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
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 361 ratings

Start your review of Machine Learning and AI Foundations: Predictive Modeling Strategy at Scale

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.