Learn the definition of big data and how it relates to machine learning.
- [Instructor] So now, we're going to cover…some critical concepts that come into play…whether your data set is large or small.…So, how much data is just enough?…Well, let's say you're trying to predict churn,…a good rule of thumb is that…you should have more than a thousand cases that churned…that you can use in training the model,…but don't forget, you also need non-churns…to compare them to.…A thousand of them would be helpful,…and also, you need that test data set.…Remember, we'll have our train and test partitions.…
So, the minimum really starts to add up.…It might surprise you how often you start to flirt…with these minimums.…Too little is more common than too much.…So, here you can see that…to meet those very basic requirements,…4,000 is a nice, round number on the low end.…As you can imagine, 4,000 total, isn't the hard part.…Sometimes, it's the 2,000 churns…because they are more rare than the non-churns.…
We don't need to go deeply into the theory behind this,…but many data scientists advocate having a third data set.…
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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