Learn about an extremely common data manipulation and why it is important for the whole team to understand, not just the modelers.
- [Instructor] Now, we're going to take…a moment to talk about an aspect of data preparation,…which might at first seem like a minor technical detail.…But pretty much everyone who's involved…in the project, both the modelers…as well as the folks that are helping…provide and prep the data need to know…about this, it's called dummy coding.…So let's say you're making a prediction,…and one of your predictors is employment category.…So here, we've got folks on salary,…we've got some independent contractors,…some part-time wage and some full-time wage.…
In general, what we're going to do now…is we're going to transform this so that whenever…they are true in a particular category,…they're going to get a one, and otherwise…they're going to get a false.…So again, we've got four salary IC we'll call it,…part-time, full-time.…What does that end up looking like?…It ends up looking like this.…So our original variable had four categories,…and now we have four new variables,…Salary Yes-No, which is true or false,…Independent Contractor Yes-No, and so on, okay?…
Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Seasonality and time alignment
- Data preparation challenges
- Data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
Skill Level Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
Defining terms1m 48s
1. The Phases of a Machine Learning Project
2. Designing a Machine Learning Dataset
3. Data Prep Challenges
4. Modeling Challenges
7. Monitoring and Maintenance
Next steps1m 1s
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