Predictive Modeling For Temporal Data

  • 10 Videos | 44m 5s
  • Includes Assessment
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Learn what is the structure of temporal data and how can we clearly define training inputs and outputs for prediction. Also learn how can we utilize feature engineering techniques to extract meaningful insights from temporal data. Finally, find out effective strategies for evaluating model performance and preparing to deploy it in the real world.

WHAT YOU WILL LEARN

  • Understand what predictive models from temporal data are
    Know how to define outcomes to predict
    Know how to find training examples
    Understand how to assemble for feature engineering
    Understand what feature engineering is
  • Know what a feature type is
    Understand the deep feature synthesis algorithm
    Know how stacking relates to deep feature synthesis
    Know how to use previously learned skills to build working model
    Know how to select a correct model

IN THIS COURSE

  • Playable
    1. 
    Introduction
    6m 57s
    UP NEXT
  • Playable
    2. 
    Defining Outcomes To Predict
    5m 9s
  • Locked
    3. 
    Searching For Training Examples
    3m 31s
  • Locked
    4. 
    Assembling Data For Feature Engineering
    4m 29s
  • Locked
    5. 
    Feature Engineering- Introduction
    3m 48s
  • Locked
    6. 
    Feature Types
    5m 1s
  • Locked
    7. 
    Deep Feature Synthesis- Primitives And Algorithm
    5m 4s
  • Locked
    8. 
    Deep Feature Synthesis- Stacking
    2m 54s
  • Locked
    9. 
    Modeling and Evaluating
    4m 29s
  • Locked
    10. 
    Model Selection
    2m 44s