Loading...

Course Description

 

500 million tweets, 6,300 scientific publications and 800 Wikipedia articles are published daily. 

The troves of language corpora create one of the most fundamental opportunities in machine learning - the ability to connect computing systems more directly to human interaction. In this course, you will learn how to apply Natural Language Understanding models in a variety of real-world scenarios.

By the end of this course, you will be able to answer questions like: How does the Washington Post recommend related articles? How does Gmail detect spam in your inbox? How does Alexa know what you mean?

Note: This workshop is recommended for students who have successfully completed the Data Science Certificate or who have a background in machine learning with Python.

 

Course Objectives

At the completion of this course, you will be able to utilize Python to build end-to-end Natural Language Understanding pipelines. This course covers how to:

  • Preprocess and transform text data for machine learning

  • Build classification models on free-text documents

  • Generate numerical “embedded” representations of text data

  • Leverage embeddings for document similarity search and topic modeling

  • Combine Natural Language Understanding methods to build a chatbot

Notes

Enrollment in this course is open to all students and applies CEUs toward the Data Science or Machine Learning track.

 

Course Prerequisites

Experience Prerequisites:

  • A bachelor's degree or equivalent

  • Successful completion of an introductory machine learning course, like Data Analysis II: Machine Learning (XBUS-505)

  • Experience using the Python programming language for data analytics

 

Hardware Requirements:

  1. A laptop with at least a dual core 1.8 GHz processor, 2GB of RAM and 1 GB free hard disk space (e.g. a laptop purchased in the past two years).

  2. A modern operating system: Windows 10 or newer, OS X 10.13 or newer, or Ubuntu 16.04 or newer, or the equivalent.

  3. Before class, you should download and install Python 3.7 or later (either the native or Anaconda distributions) along with either pip or conda to install third party dependencies such as scikit-learn and nltk. 

Applies Towards the Following Certificates

Loading...

 

Thank you for your interest in this course. Unfortunately, the course you have selected is currently not open for enrollment. Please complete a Course Inquiry so that we may promptly notify you when enrollment opens.
Required fields are indicated by .