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Code along: machine learning

Cultural Data Analysis · Week 3: Structured Data and Machine Learning

Interactive tools from this lecture: Term frequency · How a computer sees an image · Neural network training

This week's workshop: Data critique and machine learning

Seven exercises from today's slides, in the order they come up in the lecture. K-means by hand runs in Python Tutor, as in Week 1, in three small steps. The other exercises use scikit-learn and pandas, which Python Tutor cannot load, so they run in Google Colab. Open both now and sign in to Colab with a Google account.

Twice in the lecture, a Let's try slide sends you to the lecture notebook: first for its supervised half (slide 44), then for its unsupervised half (slide 64). Open it once now and choose File → Save a copy in Drive, so that your changes are kept. The boxes below show where each half comes. The slides show the number of each exercise in their top corner.

There are two kinds of exercise:

• Type and trace (Python Tutor): short pieces of plain Python. Predict what will happen, type the code by hand (the typos are part of the lesson), then click Visualize Execution and press Next to step through it.
• Copy and run (Google Colab): longer code with libraries such as pandas and scikit-learn. Predict the result, then copy the code into Colab and run it. Read the New note: it explains the lines you have not seen before. You don't need to be able to write the rest yet.

Then check your result against the expected output, and try the follow-up under Then.

Contents

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The teens data (30,000 profiles, 40 columns) is at github.com/goto4711/cdai. Python Tutor: pythontutor.com · Google Colab: colab.research.google.com