Predictive Modeling For Temporal Data
Everyone
- 10 videos | 44m 5s
- Includes Assessment
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
-
6m 57sLearn what predictive models from temporal data are FREE ACCESS
-
5m 9sLearn about defining outcomes to predict. FREE ACCESS
-
3. Searching For Training Examples3m 31sLearn about training examples and how to find them FREE ACCESS
-
4. Assembling Data For Feature Engineering4m 29sLearn about getting data together for feature engineering FREE ACCESS
-
5. Feature Engineering- Introduction3m 48sFind out what feature engineering is FREE ACCESS
-
6. Feature Types5m 1sLearn more about the specifics of your data FREE ACCESS
-
7. Deep Feature Synthesis- Primitives And Algorithm5m 4sLearn about the Deep Feature Synthesis algorithm FREE ACCESS
-
8. Deep Feature Synthesis- Stacking2m 54sLearn why it is called deep feature synthesis FREE ACCESS
-
9. Modeling and Evaluating4m 29sUse what you have learned to creat a predictive model FREE ACCESS
-
10. Model Selection2m 44sLearn about model validation and selection FREE ACCESS
