Explore a high-level review of the modeling process.
- [Instructor] I've mentioned that I think…that most folks overestimate the scaling concerns…regarding the modeling phase.…I want to walk through the modeling phase…from the point of view of the modeler…to put that comment into proper context.…At the beginning of the modeling phase,…an organized modeler should have a list…of all of the different algorithms and settings…that they're going to try.…Now, I wanted a manageable list for our purposes here,…but keep in mind that the list…would be several dozen,…and very possibly over 100 combinations…of algorithms and settings.…
So let me give you a sense…of the kinds of things you'd be experimenting with.…You'd certainly do more than one algorithm.…Most modelers are going to have access to many of these.…Here, I've listed C&RT, Support Vector Machine,…Neural Net, and Logistic Regression,…but there are many others.…Then, the modeler's going to be changing settings, as well,…so for instance, in Support Vector Machines,…there's something very technical…called the regularization parameter.…
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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