Text Mining with Machine Learning and Python [Video]

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Text Mining with Machine Learning and Python [Video]

Thomas Dehaene

Monday, April 30, 2018

New Release!

Get high-quality information from your text using Machine Learning with Tensorflow, NLTK, Scikit-Learn, and Python

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Video Details

ISBN 139781789137361

Course Length2 hours and 26 minutes

Video 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.

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.

Getting Started with Text Mining

The Course Overview

Understanding Modern-Day Text Mining

Exploring Your Text Mining Toolbox

Setting Up Your Working Environment

A Short Rundown of the Topics We Will Cover

Reading and Processing Text Features

Understanding Text Data Sources

Cleaning Messy Text

Tokenization, POS Tagging, and Lemmatization

Dealing with N-Grams

Extracting from Text

Word Search Versus Entity Extraction

Named Entity Recognition (NER)

Using Pre-Trained Models

Training Your Own NER

Deep Learning Approach to NER

Classification of Text

Feature Representation

Machine Learning Algorithms for Text Classification

Setting Up a Basic Text Classifier

Pitfalls and Rules of Thumb

Putting Classifiers into Production

Deep Learning Approach to Text Classification

Word Embeddings

What Are Word Embeddings?

Main Techniques

Training a Word2Vec Model

Visualizing a Trained Word Embedding Model

X2Vec

What You Will 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

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.

Getting Started with Text Mining

The Course Overview

Understanding Modern-Day Text Mining

Exploring Your Text Mining Toolbox

Setting Up Your Working Environment

A Short Rundown of the Topics We Will Cover

Reading and Processing Text Features

Understanding Text Data Sources

Cleaning Messy Text

Tokenization, POS Tagging, and Lemmatization

Dealing with N-Grams

Extracting from Text