Video Description
Use Python and the Natural Language Toolkit to perform various NLP Tasks. Create Chatbots, text analyzers, classifiers, and more
About This Video
- Easy-to-follow unique tutorial that shows you how to make your machine understand your language
- Make your machine read, understand, and interpret natural language with the help of powerful algorithms and tools
- Master parts of speech and semantic analysis, and resolve ambiguity with real-world examples
In Detail
Natural Language Processing (NLP) offers powerful ways to interpret and act on spoken and written language. It can help you with tasks such as customer support enquiries and customer feedback analysis. As the quantity of data continues to grow at an incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations.
This course will help you gain this skill by practical demonstrations, clear explanations, and interesting real-world examples. It will give you a versatile range of deep learning and NLP skills that you can put to work in your own applications.
By the end of this tutorial, you’ll have a better understanding of NLP and will be able to transform data into actionable knowledge. You will also have worked on multiple examples that implement deep learning to solve real-world spoken-language problems.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Natural-Language-Processing-in-Practice
Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
Table of Contents
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Chapter 1 : Getting Started with Natural Language Processing
- Course Overview 00:03:44
- Setup and Installation 00:04:26
- Understanding NLP and Its Benefits 00:05:18
- Exploring NLP Tools and Libraries 00:06:30
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Chapter 2 : Dive into NLTK
- Tokenization 00:06:39
- Stop Words 00:05:32
- Part of Speech Tagging 00:03:43
- Stemming and Lemmatization 00:04:56
- Named Entity Recognition 00:03:24
- TF-IDF 00:05:28
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Chapter 3 : Building a Sentiment Analyzer
- Introduction to Sentiment Analysis 00:01:29
- Pre-Processing the Dataset 00:05:52
- Word Embeddings 00:02:13
- Build the Network 00:01:21
- Train the Model 00:02:07
- Test the Model 00:01:00
- Apply to a Single Input 00:02:02
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Chapter 4 : Creating a Classifier
- Machine Learning 00:08:12
- Classification 00:05:16
- Pre-Processing the Dataset 00:05:52
- Naïve Bayes and SVM 00:01:22
- Train the Classifier 00:03:09
- Test the Classifier 00:02:42
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Chapter 5 : Build Your Chatbot
- Chatbots 00:03:05
- Simple NLTK Bot 00:02:28
- Create a ChatterBot 00:03:26
- Enhancing the Chabot 00:01:32
- Training the Chabot 00:04:39