Hear an overview of the content that will be covered throughout this course.
- [Instructor] Now we're going to do a high level review…of supervised machine learning,…which essentially is taking historical data…to build a model which we then score on new data.…So let's take a closer look.…In our historical data…we have to have an established outcome.…So if we're talking about loans,…they paid or perhaps they defaulted on their mortgage.…We need that end result to be known.…Then we need predictors.…There can be numerous predictors.…
Hopefully we have dozens or hundreds of them.…Examples could include things like this.…Whether or not the loan in question…is their primary mortgage…or maybe it's a second mortgage.…Or what percentage of their income…is being paid to housing.…And this can help us predict that end result.…So in order to do it.…In order to map those predictors…to the end result,…we need a modeling algorithm.…And modelers are experts in this.…And there's going to be many of them…that they might draw upon.…They have names like Decision Tree,…Support Vector Machine, Logistic Regression,…
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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