Description
Course info
Level
Intermediate
Updated
Dec 17, 2019
Duration
1h 54m
Description

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.

About the author
About the author

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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Michael Heydt, and welcome to my course, Building Features from Text Data in Microsoft Azure. I'm an independent software and cloud consultant, author, and trainer, and I am Microsoft certified in artificial intelligence, data science, and also a Microsoft Certified Trainer. One of the hottest applications of machine learning and artificial intelligence is using text data to make decisions. In this course, you will obtain the ability to structure data several ways that are usable in machine learning models. Some of the major topics that we will cover include how to use natural language processing to prepare text data where you will learn to leverage several NLP technologies such as document tokenization, stop word removal, frequency filtering, stemming and lemmatization, parts of speech tagging, and n‑gram identification. Next, you will learn to model documents as text features where you will learn to represent documents as feature vectors. Techniques that we will cover include one hot and frequency‑based encoding, word embeddings, hashing and locality sensitive hashing, and using BERT to create word embeddings, and you'll learn to do this all using machine learning services available in Azure. By the end of this course, you will have the skills and knowledge to use textual data and Microsoft Azure in conceptually sound ways to create text features for machine learning models. Before beginning the course, you should be familiar with Python, Azure, and have fundamental knowledge of feature modeling for machine learning. I hope you'll join me on this journey to learn how to create text features for machine learning in Azure with the Building Features from Text Data in Microsoft Azure course, at Pluralsight.