Discover why traditional techniques like binary logistic regression and decision trees continue to be popular, despite the development of newer and more complex techniques.
- [Instructor] There's a good reason why…traditional machine learning methods…have stood the test of time.…They still work quite well…and they tend to be highly scalable.…Let's take a look.…One of advantage of these more traditional techniques…like logistic regression let's say or decision trees…is that they're very transparent.…They really tell you a story about the data.…Also, they're fast.…They're fast models to build generally speaking…and they're fast at scoring.…Also, super important to us in this course,…they're easy to migrate.…
These are straightforward formulas,…so it's not ridiculous even to contemplate…manually moving it from one system to another…or writing a simple parser to do so.…For all these reasons,…they are still among the most common choices.…They might not be the newest or the most exciting,…but they really do work in many situations.…Let's go a level deeper.…Our statistical models,…again like binary logistic regression is a super common one,…are going to produce algebra-like formulas.…
They're going to have as many of those coefficients…
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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