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Fix: Replace unstable Giphy embeds with direct links #165

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Merged
JakobKlotz merged 1 commit into from
Feb 20, 2026
Merged

Fix: Replace unstable Giphy embeds with direct links #165

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2 changes: 1 addition & 1 deletion docs/data-science/algorithms/supervised/classification.md
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Expand Up @@ -102,7 +102,7 @@ squares, logistic regression typically uses Maximum Likelihood Estimation
likelihood of the observed data.

<div style="text-align: center;">
<iframe src="https://giphy.com/embed/3ohs7KViF6rA4aan5u" width="480" height="355" style="" frameBorder="0" class="giphy-embed" allowFullScreen></iframe>
<img src="https://media.giphy.com/media/3ohs7KViF6rA4aan5u/giphy.gif" width="480" heigh="355">
<figcaption>Lo and behold, even more math...</figcaption>
</div>

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10 changes: 2 additions & 8 deletions docs/data-science/algorithms/supervised/tree-based/cart.md
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Expand Up @@ -66,10 +66,7 @@ which is a classic binary classification task.

<div style="text-align: center">
<h3>Excited for some theory?</h3>
<iframe
src="https://giphy.com/embed/zZC2AqB84z7zFnlkbF" width="360" height="360"
style="" frameBorder="0" class="giphy-embed" allowFullScreen>
</iframe>
<img src="https://media.giphy.com/media/zZC2AqB84z7zFnlkbF/giphy.gif" width="360" height="360">
</div>

## Theory
Expand Down Expand Up @@ -461,10 +458,7 @@ data well.

<div style="text-align: center">
<h3>Now get to the point!</h3>
<iframe
src="https://giphy.com/embed/l1AsRdc4mnfIB6OhW" width="480" height="360"
style="" frameBorder="0" class="giphy-embed" allowFullScreen>
</iframe>
<img src="https://media.giphy.com/media/l1AsRdc4mnfIB6OhW/giphy.gif" width="480" height="360">
</div>

In practice, you have to find the right parameters to balance model complexity
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5 changes: 1 addition & 4 deletions docs/data-science/algorithms/unsupervised/clustering.md
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Expand Up @@ -371,10 +371,7 @@ cluster_indices = kmeans.fit_predict(X)

<div style="text-align: center;">
<h4>Now to the fun part!</h4>
<iframe
src="https://giphy.com/embed/LpXU1smaWaVwR2v6Gu" width="348" height="260"
style="" frameBorder="0" class="giphy-embed" allowFullScreen>
</iframe>
<img src="https://media.giphy.com/media/LpXU1smaWaVwR2v6Gu/giphy.gif" width="348" heigh="260">
</div>

The goal of this exercise is to recommend a song based on a previous track. The
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3 changes: 1 addition & 2 deletions docs/data-science/data/preparation.md
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Expand Up @@ -370,8 +370,7 @@ With `#!python index=False`, we do ==not==
> <cite>[pandas `to_csv()` docs](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html)</cite>

<div style="text-align: center;">

<iframe src="https://giphy.com/embed/3oKIPf3C7HqqYBVcCk" width="480" height="269" style="" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/reactionseditor-3oKIPf3C7HqqYBVcCk"></a></p>
<img src="https://media.giphy.com/media/3oKIPf3C7HqqYBVcCk/giphy.gif" width="480" height="269">
<figcaption>
Congratulations! 🎉 You have finally completed your quest to merge the
data.
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6 changes: 3 additions & 3 deletions docs/data-science/data/preprocessing.md
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Expand Up @@ -121,10 +121,10 @@ print(data.isna().sum().sum())

The output once more indicates that the whole data set has `#!python 0` missing
values. So far so good, but this is not the end of the story (who saw that
coming 🤯).
coming :exploding_head:).

<div style="text-align: center;">
<iframe src="https://giphy.com/embed/aWPGuTlDqq2yc" width="480" height="254" style="" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/celebrity-reshuffle-aWPGuTlDqq2yc"></a></p>
<img src="https://media.giphy.com/media/aWPGuTlDqq2yc/giphy.gif" width="480" height="254">
<figcaption>
Plot twist...
</figcaption>
Expand Down Expand Up @@ -451,7 +451,7 @@ uv add scikit-learn
```

<div style="text-align: center;">
<iframe src="https://giphy.com/embed/xT5LMxAxpGSb5AZt8A" width="480" height="362" style="" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/season-5-the-simpsons-5x8-xT5LMxAxpGSb5AZt8A"></a></p>
<img src="https://media.giphy.com/media/xT5LMxAxpGSb5AZt8A/giphy.gif" width="480">
<figcaption>
<code>scikit-learn</code> the swiss-army knife for data
preprocessing and machine learning in Python.
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