# Getting Started With ML

**URL:** <https://forum.knime.com/t/getting-started-with-ml/26531>\
**Category:** KNIME Analytics Platform\
**Tags:** machine-learning, text-processing\
**Created:** [September 4, 2020, 4:33pm UTC](https://forum.knime.com/t/getting-started-with-ml/26531 "2020-09-04T16:33:56Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![ScottF](https://forum-cdn.knime.com/user_avatar/forum.knime.com/scottf/32/61316_2.png) [@ScottF](https://forum.knime.com/u/ScottF)\
**Post date:** [September 4, 2020, 5:37pm UTC](https://forum.knime.com/t/getting-started-with-ml/26531/3 "2020-09-04T17:37:39Z")

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Another approach here might be to use the [Similarity Search](https://kni.me/n/LAcCo6A4MFNixQyw) node with Levenshtein distance - you feed the misspellings to the top port and your dictionary to the middle port, and let the node find the best match.

I just made a simple workflow (based on an exercise from our L1-DW course) that uses your data:

> **[Simple Fuzzy Match Example – scottf](https://hub.knime.com/scottf/spaces/Public/latest/ForumWorkflows/2020/09/Simple%20Fuzzy%20Match%20Example)**
>
> This workflow reads in a series of misspelled retailer names, along with the desired version of the names. Using the Similarity Search node, the closest correc…

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