- [Instructor] I want to thank you for taking the course.…Let's discuss some options…for what you might want to check out next.…If you are IT or IT management,…I definitely recommend Dan Sullivan's course…right here in the library,…Deploying Scalable Machine Learning for Data Science.…If you're analytics management or even senior management,…I would check out my course, The Essential Elements…of Predictive Analytics and Data Mining.…If you're a modeler, and you want to get deeper…into the algorithms, a great place to start…is my course, Machine Learning…and AI Foundations, Classification Modeling.…
If on the other hand, you have a real interest…in specific algorithms, there are numerous courses…in the library that give you a deep dive…into the specific modeling approaches.…I have a number of them myself, including courses…on regression, cluster analysis, and decision trees.…
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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