Level: Intermediate to Advanced
Prerequisite: See below
Despite predictions to the contrary, textual data has grown exponentially in recent years. Whether coming from documents, blogs, social media posts, or customer service chats, not only has the volume of textual data increased but so has its potential value.
However, there’s a problem.
From the perspective of analytics and data science, textual data is unstructured. Text analytics techniques take unstructured raw text data and transform it into representations useful for analysis and machine learning.
This course is a hands-on introduction to text analytics using Python. Students will learn the fundamentals of building pipelines that clean and transform text documents into formats that can be fed to clustering and machine learning algorithms.
Text analytics is the foundation of some of the most recent advancements in artificial intelligence (AI), such as large language models like ChatGPT.
Although this course contains some mathematics, the level of math is accessible to a broad audience and the focus is on concepts, not calculations.
This is part of an optional Machine Learning Bootcamp. Learn more about the courses offered, or attend this individual course.
You Will Learn
- How to represent text documents using the bag of words model
- Tokenization
- Stopword removal
- Stemming and lemmatization
- Part-of-speech (POS) tagging
- N-grams
- Term frequency-inverse document frequency (TF-IDF)
- How to group text documents based on similarity
- How to classify text documents
- Additional resources for honing skills
Geared To
- Business and data analysts
- BI and analytics developers and managers
- Business users
- Aspiring data scientists
- Anyone interested in using text analytics with their business data
Prerequisites
Registrants must be familiar with Python and Python Notebooks or complete a complimentary online course before the conference. Access to the 3-hour online course "Python Quick Start" will be provided to registrants three weeks prior to the event.
No background in advanced mathematics or statistics is required.
Laptop Setup
Students must bring their laptops to class.
Note on Corporate Laptops:
This course requires installation of software, as well as the ability to download data files, library files, and code.
If your corporate laptop blocks these activities:
- Contact your IT department early for assistance in preparing your laptop
- Or bring and use a personal device instead
Machine Requirements:
- Windows or Max OS X
- 64-bit operating system
- 8 GB available RAM, 16 GB preferred
- 4 GB of HD space for Anaconda Python installation
Anaconda Python is used in this course because it is free, easy to install, and has all the needed libraries.
Setup:
Laptop setup is required BEFORE the conference. Instructions will be emailed to registrants before the event.
There is no time allotted in class for laptop preparation.
* Enrollment is limited to 40 attendees.