About The Course:
Text is one of the dominant sources of information across the world. But most of it is unstructured and mining high-quality information from such text becomes increasingly critical. Text analytics plays an important role in discovering patterns and information from textual data. The techniques for text analytics go beyond frequency count and also involve complex algorithms from Natural Language Processing (NLP) and Machine Learning.
This course aims to provide learners an understanding of the methods for text analytics. It will cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making. The techniques will include Named Entity Recognition, Sentiment Analysis and Text Categorization among others. Learners will also be introduced to various open source utilities for developing text analytics applications.
Objectives: By the end of the course, learners can:
Target audience: Anyone interested in text analysis involving large amount of unstructured data, developers and researchers of data science. A basic knowledge of any programming language is a prerequisite for this course.
Topics to be covered:
|Classroom and Lab||3 days, 15 hours (Classroom) and 8 hours (Lab)||June 16 - 18, 2016|