by Alexis Rutherford | Released March 2018 | ISBN: 9781789139938

Practical techniques and methods to analyze your text data. Power your decision making using next generation libraries.About This VideoGet to grips with powerful new libraries such as Gensim, Spacy, and KerasLearn tips and tricks to quickly become an NLP practitionerLearn fundamental concepts and get hands-on worked examplesIn DetailThe company you work for has accumulated a lot of valuable data from its customers, all stored as text, and you need to extract some value from that data. You've spent a lot of combined time writing about what they want but no-one knows what they have written about and no-one has the time to read all the messages.This course empowers you to know how to attack this and other text analysis problems to unlock value for your organization. You'll start by seeing how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. Then we'll show you how to solve a practical problem using NLP by building a spam SMS detector. You'll also learn to convert words into numbers that can be analyzed.Moving on, we'll teach you how to accurately label new documents to get an accuracy score and cluster your data together. Finally, you'll see more advanced analysis and will model text by using vector space models and semantic parsing to break down the components of a sentence. You'll also work with neural networks and learn how to write believable text.By the end of the course, you'll be able to use the latest libraries of NLP in Python in your day-to-day tasks. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Next-Generation-Natural-Language-Processing-with-Python Show and hide more

1. Chapter 1 : Getting Started with Natural Language Processing (NLP)
   • The Course Overview 00:04:03
   • NLP and Its Uses 00:05:47
   • Statistical Analysis of Language – Counting Versus Understanding 00:05:16
   • Exploring Different Types of Text Data 00:04:08
   • NLP Libraries in Python and Installation 00:05:12
2. Chapter 2 : A First Text Classifier – Detecting Spam SMS Messages
   • Finding and Loading Spam SMS Data 00:02:51
   • Preparing SMS Data for Analysis and Training a Classifier 00:04:29
   • Classifying Messages, Evaluating, and Testing 00:03:32
3. Chapter 3 : Text Tricks, Definitions, and Heuristics
   • Understanding Text as Noisy Data 00:07:33
   • Splitting Documents into Parts 00:02:24
   • Turning Words into Numbers 00:06:29
4. Chapter 4 : Categorizing Text Documents
   • Supervised Learning Refresher 00:03:52
   • Supervised Learning Refresher (Continued) 00:06:45
   • Building a Pipeline in scikit-learn to Categorize News Articles 00:04:28
   • Optimizing a Classifier Using GridSearchCV 00:05:58
   • Deploying a Trained Model in Production 00:05:41
5. Chapter 5 : Unsupervised Learning for Text
   • Finding Structure in a Text Corpus 00:08:16
   • Understanding Gensim for Efficient Topic Modelling 00:04:03
   • Creating a Corpus and Extracting Topics 00:10:00
   • Evaluation of Topic Models 00:04:19
6. Chapter 6 : Implementing Advanced NLP Methods
   • Working with Vector Space Models 00:07:29
   • Implementing Semantic Parsing 00:03:27
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