Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning terminology, and discuss the four key requirements for working with data in scikit-learn.

Download the notebook: https://github.com/justmarkham/scikit-learn-videos
Iris dataset: http://archive.ics.uci.edu/ml/datasets/Iris
scikit-learn dataset loading utilities: http://scikit-learn.org/stable/datasets/
Fast Numerical Computing with NumPy (slides): https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015
Fast Numerical Computing with NumPy (video): https://www.youtube.com/watch?v=EEUXKG97YRw
Introduction to NumPy (PDF): http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf

WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS:

1) WATCH my scikit-learn video series:
https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A

2) SUBSCRIBE for more videos:
https://www.youtube.com/dataschool?sub_confirmation=1

3) JOIN "Data School Insiders" to access bonus content:
https://www.patreon.com/dataschool

4) ENROLL in my Machine Learning course:
https://www.dataschool.io/learn/

5) LET'S CONNECT!
- Newsletter: https://www.dataschool.io/subscribe/
- Twitter: https://twitter.com/justmarkham
- Facebook: https://www.facebook.com/DataScienceSchool/
- LinkedIn: https://www.linkedin.com/in/justmarkham/