3.2 KiB
Clustering models for machine learning
Clustering na machine learning waka weh e dey look for tins weh dey look like each oda and put dem together for groups weh dem dey call clusters. Wetin make clustering different from oda ways for machine learning na say e dey happen automatic, true true, e fit be say e be opposit of supervised learning.
Regional topic: clustering models for a Nigerian audience's musical taste 🎧
Nigeria get plenty kind different people wey get different music weh dem like. Using data we dem scrap for Spotify (wey come from this article, mek we check some music wey people for Nigeria dey like. Dis data get info about different songs danceability score, acousticness, loudness, speechiness, popularity and energy. E go sweet to find wetin dey common for dis data!
Photo by Marcela Laskoski on Unsplash
For dis lesson series, you go learn new ways to sabi data using clustering ways. Clustering dey very useful when your data no get labels. If e get labels, den classification ways like dem wey you don learn before fit make sense pass. But if you dey find to group data wey no get label, clustering na beta way to find different pattern.
E get beta low-code tools wey fit help you sabi how to work with clustering models. Try Azure ML for this task
Lessons
Credits
Dis lessons dem write with 🎶 by Jen Looper wit helpful reviews from Rishit Dagli and Muhammad Sakib Khan Inan.
The Nigerian Songs data dem collect am for Kaggle as e come from Spotify.
Useful K-Means examples wey help dem create dis lesson na dis iris exploration, dis introductory notebook, and dis hypothetical NGO example.
Disclaimer: Dis document don translate wit AI translation service Co-op Translator. Even tho we dey try make am correct, abeg make you know say automated translation fit get errors or mistakes. Di original document for dia own language na im be di correct source. For important info, make person wey sabi human translation do am. We no go responsible for any misunderstanding or wrong understanding wey fit happen because of dis translation.