Join Keith McCormick for an in-depth discussion in this video Next steps, part of Machine Learning and AI Foundations: Predictive Modeling Strategy at Scale.
- [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.…
Author
Keith McCormick
Released
12/11/2018Note: 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
Duration
Views
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Introduction
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Defining terms1m 48s
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1. The Phases of a Machine Learning Project
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2. Designing a Machine Learning Dataset
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How much data do I need?2m 12s
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Balancing1m 56s
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Who truly has big data?3m 43s
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Assessing data3m 32s
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3. Data Prep Challenges
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Data and the data scientist2m 55s
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Dummy coding2m 1s
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Feature engineering2m 51s
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4. Modeling Challenges
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Slow algorithms: Brute force1m 59s
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Slow algorithms: More models2m 24s
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How to sample properly2m 36s
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Modeling with missing data3m 37s
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5. Scoring
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Scoring a black box model2m 50s
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Scoring an ensemble1m 49s
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6. Deployment
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Batch vs. real-time scoring4m 39s
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Data prep and scoring2m 59s
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7. Monitoring and Maintenance
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Conclusion
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Next steps1m 1s
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Video: Next steps