How to Get Started with Machine Learning in Python

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The Python conference PyCon2014 has held recently and the videos for the conference are online.

I have been working my way through the interesting machine learning ones and will share a few on this over the coming weeks.

A great talk if you are starting out in data science or machine learning in python was given by Melanie Warrick titled How to Get Started with Machine Learning. It’s about 25 minutes long. The abstract of the talk is:

Provide an introduction to machine learning to clarify what it is, what it’s not and how it fits into this picture of all the hot topics around data analytics and big data.

Melanie starts out with a great definition of machine learning, pointing to Arthur Samuel:

Computers…ability to learn without… explicit programming

She positions machine learning as the toolkit used in Artificial Intelligence and Data Science. Relatedly, she describes big data as data beyond the ability of common technology to capture and curate. This definition sits well with me. Although the talk is an introduction to machine learning, the focus is on the application of machine learning in data science.

Melanie describes the four main data science roles as data lead, data creative, data developer and data researcher and uses a graph to indicate the amount of machine learning performed by each role. She also describes a data science project workflow.

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data science project flow

Data Science Project Flow by Melanie Warrick.

She provides a cute example of linear regression on a 2d dataset (head size vs brain weight) using scikit-learn. Usefully, she summarizes Python tools in categories:

  • Explore data: pandas, statsmodels, matplotlib, numpy, unix
  • Build model: scikit-learn, numpy, pandas, scipy
  • Test model: scikit-learn, matplotlib
  • Data products: API, Flask, Django
  • Visualize: D3, Matplotplib, vincent and vega, ggplot

There is also a question at the end about contracting Python and R and she makes the apt comment of sticking with one language (i.e. Python) so you don’t need to change languages between research and production.

The talk is on youtube and on the pyvideo archive. You can review the slides from the talk and download the sample code from github. Melanie maintains a blog at nyghtowl.io and you can review the post on her talk here.

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6 Responses to How to Get Started with Machine Learning in Python

  1. Federico Pascual April 25, 2014 at 1:15 am #

    Nice video and useful article for people looking to get started in machine learning + python. If you are interested in Machine Learning you should check out our new text mining toolkit, we are in private alpha: http://www.monkeylearn.com/ maybe our tool will be handy to you.

    • jasonb April 25, 2014 at 9:43 am #

      Very cool tool, thanks for sharing. I’d love to have a play with it. Shout if I can get access to the private beta.

      • Federico Pascual April 29, 2014 at 1:45 am #

        Hey Jason, we would LOVE to have your feedback! We will send you access to private alpha in a couple of days (we received your request for an invite so we have your email). Thank you for your interest 🙂

        • Federico Pascual May 16, 2014 at 12:37 am #

          Hey Jason, we sent you an invite for MonkeyLearn private alpha. Please let us know if you have any questions! Cheers

  2. Florian April 25, 2014 at 8:29 pm #

    Sounds great…in my cs classes for ai we just get thrown with Python and the Libs for it. Learning more context for this topic should help understanding it.

  3. Jesús Martínez March 29, 2018 at 6:01 am #

    Great video. Thanks for sharing!

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