Explore different snapshots of data at different stages of the process.
- [Narrator] So let's follow the data…through the whole process.…When the modeler begins, their almost always starting…by looking at lots of transactions.…So for instance, we have a bunch of items…that people have bought,…and we can see that several rows…are associated with one customer.…What they're looking for at this stage,…is things as simple as,…how long is the maximum length of time…a customers been around?…Did they buy just a few things,…or did they buy a lot of things?…Because, what we want to zero in on…is the individual customer level.…
So here we're looking at just one customer.…Mr. Streeter, who lives on Rockwell Lane,…and what we have to now do, is pull in…all the transactions that belong to just him.…So our data set is about to get shorter,…but much wider,…let's see why.…It's not just all those transactions…that we're interested in, but all kinds of other things…that we can get at that individual level.…So we might have had hundred of thousands of transactions,…but associated with ten's of thousands of customers.…
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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