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District Data Labs

NLP Research Lab Part 1: Distributed Representations

How I Learned To Stop Worrying And Love Word Embeddings

Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab.

This post is about Distributed Representations, a concept that is foundational not only to the understanding of data processing in machine learning, but also to the understanding of information processing and storage . . .

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July 27, 2016

Beyond the Word Cloud

Visualizing Text with Python

In this article, we explore two extremely powerful ways to visualize text: word bubbles and word networks. These two visualizations are replacing word clouds as the defacto text visualization of choice because they are simple to create, understandable, and provide deep and valuable at-a-glance insights. In this post, we will examine how to . . .

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July 26, 2016

Visual Diagnostics for More Informed Machine Learning: Part 3

Visual Evaluation and Parameter Tuning

Note: Before starting Part 3, be sure to read Part 1 and Part 2!

Welcome back! In this final installment of Visual Diagnostics for More Informed Machine Learning, we'll close the loop on visualization tools for navigating the different phases of the machine learning workflow. Recall that we are framing the workflow in terms of the . . .

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May 25, 2016

Preparing for NLP with NLTK and Gensim

PyCon 2016 Tutorial on Sunday May 29, 2016 at 9am

This post is designed to point you to the resources that you need in order to prepare for the NLP tutorial at PyCon this coming weekend! If you have any questions, please contact us according to the directions at the end of the post.

In this tutorial, we will explore the features of the NLTK library for text processing in order to build . . .

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Posted in: nlppython

May 25, 2016

Visual Diagnostics for More Informed Machine Learning: Part 2

Demystifying Model Selection

Note: Before starting Part 2, be sure to read Part 1!

When it comes to machine learning, ultimately the most important picture to have is the big picture. Discussions of (i.e. arguments about) machine learning are usually about which model is the best. Whether it's logistic regression, random forests, Bayesian methods, support vector . . .

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May 24, 2016

Visual Diagnostics for More Informed Machine Learning: Part 1

Feature Analysis

How could they see anything but the shadows if they were never allowed to move their heads?

— Plato The Allegory of the Cave

Python and high level libraries like Scikit-learn, TensorFlow, NLTK, PyBrain, Theano, and MLPY have made machine learning accessible to a broad programming community that might never have found it otherwise. . . .

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May 19, 2016

Building a Classifier from Census Data

An end-to-end machine learning example using Pandas and Scikit-Learn

One of the machine learning workshops given to students in the Georgetown Data Science Certificate is to build a classification, regression, or clustering model using one of the UCI Machine Learning Repository datasets. The idea behind the workshop is to ingest data from a website, perform some initial analyses to get a sense for what's . . .

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May 02, 2016

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