scikit-learn

There are a ton of libraries in the python ecosystem that are perfect for data science (numpy, scipy, scikit-learn, etc).

K-d trees are even built in to SciPy, which the author probably already has installed (Scikit-Learn also has an implementation).

The scikit-learn website is chock full of tutorials meant for beginners, with code examples!

Sure, a developer will translate the end result to scikit-learn or AzureML, but this is stuff that's easily available to even mildly-technical enthusiasts.

To cover a ML technique very well, play with every single parameter of the scikit-learn functions of that technique by using the same dataset.

I wonder if nowadays that result could be replicated with readily available algorithms from scikit-learn.

All of these techniques are available in Python's ML stack, scikit-learn, NumPy, pandas, etc.

Check out scikit-learn and its excellent documentation.

I also have a background in data science, scipy, numpy, pandas, scikit-learn, nltk etc.

I would recommend starting with scikit-learn.

Also, always try to include visualization of the data (scikit-learn has examples with matplotlib to learn from how to do it) so you can actually see the changes of the implementation when parameters of the function change.

- Senior Data Engineer | Seeking an engineer with strong experience in Python and a love of gaining new insight from data using tools like Pandas, SciKit-Learn, and much more.

With some of the "data science" I see in big companies, they tend to just import the library and leave the math up to PhD's and the writers of scikit-learn.

Scikit-learn has a great documentation and many examples that will make the whole learning process exciting.