Text Mining with Machine Learning and Python

Text Mining with Machine Learning and Python

Updated Feb 01, 2019

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. 

Target Audience 

This course targets Data Scientists who need to obtain a basic set of skills in the field of text analysis, or a Citizen Data Scientist who wants to get up and running with text mining. Since this counts as a specialization course, basic knowledge of Python, Machine Learning, and Data Science are required.

Business Outcomes 

  • 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