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One of my favorite parts of machine learning in Python is that it got the benefit of observing the R community and then emulating the best parts of it.

One thing that is a blessing and a curse in R is that the machine learning algorithms are generally segmented by package.

Meaning instead of having a single (or set) of ML libraries that each implement some common algorithms, each algorithm gets its own package.

It's sort of nice because you can find very esoteric, cutting edge implementations of algorithms, but it can be a pain for day-to-day use where you might be switching between algorithms.

pandas took the best parts of data munging in R and turned it into a Python package.

And if you're in the market for some super slick, great looking interactive plots then try out bokeh.

It has a fantastic built-in regular expressions library, re, and a built-in string meta-libarary appropriately called string.

We released the very first version of Rodeo just over a year ago and released the 2.0 for Windows, OSX, and Linux about a month ago.

Jupyter notebooks provide an interactive environment for programming in Python (and other languages) that focuses on reproducibility and visualization--it even has a plugin for R!

Same concept: write SQL queries against your data frames, get data frames back!

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Machine Learning avec Spark, MLLib et D3.js

Cette conférence a pour objet de partager avec les participants le processus d'intégration d'un système de Machine Learning (ML) dans une application Java ...

[MINI] Automated Feature Engineering

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Curso de HTML5 - 06 - Parágrafos, Quebras e Símbolos Especiais - by Gustavo Guanabara

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[MINI] Feed Forward Neural Networks

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