> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/problem-framing/model-families.md).

# Model Families

Once the problem shape and the learning setting are known, the remaining choice is which algorithm family to reach for. The page gives a map from task type to algorithm family first, then a course-level reference behind it.

The same notes are in [Meta Learning](/deep-learning/meta-learning.md).

The map is SAS's "Which machine learning algorithm should I use?", designed primarily for beginner to intermediate data scientists or analysts who want to identify and apply machine learning algorithms to their own problems: [A wonderful introduction into machine learning, and how to choose the right algorithm or family of algorithms for the task at hand.](https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/?utm_source=facebook\&utm_medium=cpc\&utm_campaign=analytics-global\&utm_content=US_interests-conversions)

Behind the map sits the course material: [Stanford cs221](https://stanford.edu/~shervine/teaching/cs-221/) is the CS 221 teaching page.


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