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Foundations of Natural Language Processing

Overview

This course consists of tutorials, coding exercises and real-world projects to learn natural language processing.

Natural language processing comprises of a set of computational techniques to understand natural languages such as English, Spanish, Chinese, etc.

Primary objectives of the course

Understand what NLP is.
Learn about and implement popular NLP algorithms.
Understand NLP techniques for sentiment classification.
Understand NLP techniques for information retrieval, i.e. how search engines work.
Understand NLP techniques for topic modeling, i.e. how to perform clustering to identify related / similar documents.

Prerequisites: Python and Probability

Syllabus

Overview of NLP

This section has just one tutorial, introducing you to NLP, discussing its applications, challenges and the various approaches to NLP.

Introduction to Natural Language Processing
Free
12 mins

Information Retrieval using TF-IDF

We first introduce TF-IDF (term frequency, inverse document frequency), a very commonly used measure in NLP to weigh the importance of different words. This helps us in search engine ranking (also called document retrieval), finding similar or related documents, and so on.

Text Classification and Sentiment Analysis

Then we move on to text classification (separating different types of documents into predefined categories), and use the learned concepts to implement sentiment classification from scratch.

Topic Modeling with LDA

In this last section, we'll cover topic modeling, i.e. the problem of organizing documents into groups when predefined categories are not available.

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Introduction to Natural Language Processing

Category: Data Science and Big Data