by Thomas Dehaene | Released April 2018 | ISBN: 9781789137361

Get high-quality information from your text using Machine Learning with Tensorflow, NLTK, Scikit-Learn, and PythonAbout This VideoPragmatic approach with working examplesWork with real-life dataWork with modern and production-ready toolsCover the most relevant topics to get you startedIn DetailText 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 Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Getting Started with Text Mining
   • The Course Overview 00:04:46
   • Understanding Modern-Day Text Mining 00:04:56
   • Exploring Your Text Mining Toolbox 00:03:27
   • Setting Up Your Working Environment 00:03:35
   • A Short Rundown of the Topics We Will Cover 00:01:56
2. Chapter 2 : Reading and Processing Text Features
   • Understanding Text Data Sources 00:03:39
   • Cleaning Messy Text 00:04:53
   • Tokenization, POS Tagging, and Lemmatization 00:06:19
   • Dealing with N-Grams 00:08:39
3. Chapter 3 : Extracting from Text
   • Word Search Versus Entity Extraction 00:03:21
   • Named Entity Recognition (NER) 00:04:23
   • Using Pre-Trained Models 00:06:20
   • Training Your Own NER 00:13:59
   • Deep Learning Approach to NER 00:05:18
4. Chapter 4 : Classification of Text
   • Feature Representation 00:06:59
   • Machine Learning Algorithms for Text Classification 00:02:37
   • Setting Up a Basic Text Classifier 00:08:06
   • Pitfalls and Rules of Thumb 00:03:51
   • Putting Classifiers into Production 00:03:55
   • Deep Learning Approach to Text Classification 00:03:34
5. Chapter 5 : Word Embeddings
   • What Are Word Embeddings? 00:04:50
   • Main Techniques 00:03:35
   • Training a Word2Vec Model 00:05:58
   • Visualizing a Trained Word Embedding Model 00:04:35
   • X2Vec 00:03:44
6. Chapter 6 : Other ML Topics with Text
   • Stitching It All Together 00:02:52
   • Topic Modelling 00:02:48
   • Text Generation 00:04:58
   • Machine Translation 00:04:27
   • Further Reading 00:01:29
   • Closing 00:02:36
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