Text Mining with Machine Learning and Python [Video]

More Information
Learn
  • Refine and clean your text
  • Extract important data from text
  • Classify text into types
  • Apply modern ML and DL techniques on the text
  • Work on pre-trained models
  • Important text mining processes
  • Analyze text in the best and most effective way
About

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.

The code bundle for this video course is available at https://github.com/PacktPublishing/Text-Mining-with-Machine-Learning-and-Python

Style and Approach

A practical guide demonstrating how to extract information easily using Jupyter notebooks, Anaconda, modern packages, and tools/frameworks such as NLTK, Spacy, Gensim, Scikit-learn, Tensorflow (for CPU), and Python-CRFSuite.

Features
  • Pragmatic approach with working examples
  • Work with real-life data 
  • Work with modern and production-ready tools
  • Cover the most relevant topics to get you started
Course Length 2 hours 26 minutes
ISBN 9781789137361
Date Of Publication 29 Apr 2018

Authors

Thomas Dehaene

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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