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Introduction to Machine learning with k-nearest neighbours
May 31 @ 6:30 pm - 8:30 pm EDT
This is the first part of a multi-part hands-on meetup session.
In this meetup session, you will learn almost everything you need to know about creating a Machine learning pipeline with k-nearest neighbours (KNN), a simple, intuitive algorithm which can used for classification and regression.
We will introduce the SK-learn software stack which is the most widely used python package for data science and implements the KNN algorithm. We will use this package to build a Machine learning pipeline, including data analysis, data pre-processing, hyper-parameter selection, model training, and model validation within a Jupyter Notebook.
[Joseph Santarcangelo, Data Scientist, IBM](http://linkedin.com/in/joseph-s-50398b136)
[Richard Ye, IBM Data Science Intern](linkedin.com/in/richard-ye)
[Cindy Huang, IBM Data Science Intern](https://www.linkedin.com/in/cindy-shih-ting-huang/)
**Note:** This will be an online event. It is recommended that you [Register](https://ibm.webex.com/ibm/j.php?RGID=r5458eade5e2de87ab9487f6745aad4c0) for the event at https://ibm.webex.com/ibm/j.php?RGID=r5458eade5e2de87ab9487f6745aad4c0 to receive a calendar invitation and reminder for the session. We look forward to having you join us.