Introduction to Machine learning with k-nearest neighbours

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.

Instructors:
Joseph Santarcangelo, Data Scientist, IBM
Richard Ye, IBM Data Science Intern
Cindy Huang, IBM Data Science Intern

Note: This will be an online event. It is recommended that you Register 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.