- [Instructor] Now, we're going to talk…about what data we're going to use…and what data we're going to set aside.…I also discuss this in my Essential Elements…of Predictive Analytics course,…and there, I go into a tad bit more depth…and use a different example.…So you'll recall that the whole purpose…of building these predictive models…is to generate a score…that allows us to predict an end result.…So let's revisit this notion…that what we're tryin' to predict…is whether or not a mortgage is going to be paid on time…or someone might default.…
At deployment, we're going to let our new data…flow through the model,…and we're going to generate a score.…The rule for the data that we should include…is really as simple as this.…If it's going to be part of the scoring process,…it belongs in the data,…and if it's not going to be part of the scoring process…down the road when we score our future cases,…it doesn't belong in the data set.…A lot of times what folks will think…is that they're adding bias to the model…if they don't use all of their data.…
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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