Machine Learning in Python: Iris Classification -- Part 3
General Description:
In this video, we begin by showcasing how to build an iris classification
model, that is, a machine learning model that will allow us to classify
species of iris flowers. This application will introduce many rudimentary
features and concepts of machine learning and is a good use case for these
types of models.
Use case: Botanist wants to determine the species of an iris flower based on
characteristics of that flower. For instance attributes including petal
length, width, etc. are the "features" that determine the classification of a
given iris flower.
Part 3 Description:
We use sklearn to invoke the K-nearest neighbors algorithm to determine
whether a given sample is of a specific species of iris.
This video is part of a series on Machine Learning in Python. The link to the playlist may be accessed here:
http://bit.ly/lp_mlearn
Python Code:
Part 1: https://github.com/vprusso/youtube_tu...
Part 2: https://github.com/vprusso/youtube_tu...
Part 3: https://github.com/vprusso/youtube_tu...
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Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here:
http://bit.ly/lp_vim
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