[Pycon] [new paper] "Vaibhav Srivastava" - Demystifying Natural Language Processing using Python (Scikit-Learn/ Keras)

[info a pycon.it]

Title: Demystifying Natural Language Processing using Python (Scikit-Learn/ Keras)
Duration: 90 (includes Q&A)
Q&A Session: 0
Language: en
Type: Talk

Abstract: ### Abstract

It can be difficult to figure out how to work with text in scikit-learn, even if you're already comfortable with the scikit-learn API. Many questions immediately come up: Which vectorizer should I use, and why? What's the difference between a "fit" and a "transform"? What's a document-term matrix, and why is it so sparse? Is it okay for my training data to have more features than observations? What's the appropriate machine learning model to use? And so on...

In this tutorial, we'll answer all of those questions, and more! We'll start by walking through the vectorization process in order to understand the input and output formats. Then we'll read a simple dataset into pandas, and immediately apply what we've learned about vectorization. We'll move on to the model building process, including a discussion of which model is most appropriate for the task. We'll evaluate our model a few different ways, and then examine the model for greater insight into how the text is influencing its predictions. Finally, we'll practice this entire workflow on a new dataset, and end with a discussion of which parts of the process are worth tuning for improved performance.

### Objectives

By the end of this tutorial, attendees will be able to confidently build a predictive model from their own text-based data, including feature extraction, model building and model evaluation.

### Detailed Outline

1. Model building in scikit-learn (refresher)
2. Representing text as numerical data
3. Reading a text-based dataset into pandas
4. Vectorizing our dataset
5. Building and evaluating a model
6. Comparing models
7. Examining a model for further insight
8. Practicing this workflow on another dataset
9. Tuning the vectorizer (discussion)

Tags: [u'nltk', u'scikit-learn', u'nlp', u'cognitive-science', u'Machine Learning', u'Statistical Learning', u'computational-linguistics', u'pandas', u'Artificial Intelligence']