Machine Learning in Python: Iris Classification -- Part 1
Machine Learning in Python: Iris Classification -- Part 1
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 1 Description:
We install the sklearn module and make use of the built-in iris dataset
provided to us by sklearn. We look at the data and describe how it is
formatted within the Python dictionary that it is stored in.
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...
If I've helped you, feel free to buy me a beer :)
PayPal: https://www.paypal.me/VincentRusso1
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
If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe:
http://bit.ly/lp_subscribe
On this page of the site you can watch the video online Machine Learning in Python: Iris Classification -- Part 1 with a duration of hours minute second in good quality, which was uploaded by the user LucidProgramming 15 March 2019, share the link with friends and acquaintances, this video has already been watched 35,194 times on youtube and it was liked by 474 viewers. Enjoy your viewing!
