Using text data to make decisions is key in creating text features for machine learning models. In this course, Building Features from Text Data in Microsoft Azure, you’ll obtain the ability to structure your data several ways that are usable in machine learning models using Microsoft Azure Machine Learning Service virtual machines. First, you’ll discover how to use natural language processing to prepare text data, and how to leverage several natural language processing technologies, such as document tokenization, stopword removal, frequency filtering, stemming and lemmatization, parts-of-speech tagging, and n-gram identification. Then, you’ll explore documents as text features, where you’ll learn to represent documents as feature vectors by using techniques including one-hot and count vector encodings, frequency based encodings, word embeddings, hashing, and locality-sensitive hashing. Finally, you’ll delve into using BERT to generate word embeddings. By the end of this course, you’ll have the skills and knowledge to use textual data and Microsoft Azure in conceptually sound ways to create text features for machine learning models.
Author Name: Michael Heydt
Author Description:
Mike is a seasoned software developer, IT guy, cloud architect, IoT fanatic, and overall gadget hound. He is currently a freelance developer, DevOps engineer, author, trainer, and speaker. Mike has worked in many industries including finance/trading systems, cable television/interactive TV, GIS, healthcare, social media, and genomics. Mike’s background has historically been in Microsoft solutions, having built those systems since the days of DOS all the way through .NET and Azure. He holds ove… more

Table of Contents

• Course Overview
  1min
• Processing and Simplifying Text to Simplify Feature Creation
  54mins
• Building Features Around Text Data for Use in Machine Learning Models
  58mins