Text Mining with Machine Learning and Python

Description

Text is one of the most actively researched and widely spread types of data in the Data Science field today. New advances in machine learning and deep learning techniques now make it possible to build fantastic data products on text sources. New exciting text data sources pop up all the time. You'll build your own toolbox of know-how, packages, and working code snippets so you can perform your own text mining analyses.

You'll start by understanding the fundamentals of modern text mining and move on to some exciting processes involved in it. You'll learn how machine learning is used to extract meaningful information from text and the different processes involved in it. You will learn to read and process text features. Then you'll learn how to extract information from text and work on pre-trained models, while also delving into text classification, and entity extraction and classification. You will explore the process of word embedding by working on Skip-grams, CBOW, and X2Vec with some additional and important text mining processes. By the end of the course, you will have learned and understood the various aspects of text mining with ML and the important processes involved in it, and will have begun your journey as an effective text miner.

About the Author

Thomas Dehaene is a Data Scientist at FoodPairing, a Belgium-based Food Technology scale-up that uses advanced concepts in Machine Learning, Natural Language Processing, and AI in general to capture meaning and trends from food-related media. He obtained his Master of Science degree in Industrial Engineering and Operations Research at Ghent University, before moving his career into Data Analytics and Data Science, in which he has been active for the past 5 years. In addition to his day job, Thomas is also active in numerous Data Science-related activities such as Hackathons, Kaggle competitions, Meetups, and citizen Data Science projects.

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