Machine learning educational resources (wip)

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# Quick utility to embed the videos below
from IPython.display import YouTubeVideo
def embed_video(index, playlist='PLeFIaIQF2TkB04NMOWoj3vyBa58LdoRLe'):
    return YouTubeVideo('', index=index - 1, list=playlist, width=600, height=350)

Part 1: Loading and Visualizing Data

In this video, I introduce the dataset, and use the Jupyter notebook to download and visualize it.

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Part 2: Further Data Exploration

In this video, I do some slightly more sophisticated visualization with the data, using matplotlib and pandas.

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Part 3: Version Control with Git & GitHub

In this video, I set up a repository on GitHub and commit the notebook into version control.

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Part 4: Working with Data and GitHub

In this video, I refactor the data download script so that it only downloads the data when needed

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Part 9: Further Data Exploration: PCA and GMM

In this video, I apply unsupervised learning techniques to the data to explore what we can learn from it

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Part 10: Cleaning-up the Notebook

In this video, I clean-up the unsupervised learning analysis to make it more reproducible and presentable.

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This post was composed within an IPython notebook; you can view a static version here or download the full source here.

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