Natural Language Processing in Practice

Natural Language Processing in Practice
Natural Language Processing in Practice
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 47m | 426 MB

Use Python and the Natural Language Toolkit to perform various NLP Tasks. Create Chatbots, text analyzers, classifiers, and more

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.

This comprehensive course will get you get up-and-running with Natural Language Processing algorithms and building networks in Python. The course contains examples, teaching you to build-as-you-learn.

What You Will Learn

  • Build applications with Python, using the Natural Language Toolkit via NLP
  • Create your own Chatbot using NLP
  • Perform several Natural Language Processing tasks
  • Classify text and speech using the Naive Bayes Algorithm
  • Use various tools and algorithms to build real-world applications
Table of Contents

01 Course Overview
02 Setup and Installation
03 Understanding NLP and Its Benefits
04 Exploring NLP Tools and Libraries
05 Tokenization
06 Stop Words
07 Part of Speech Tagging
08 Stemming and Lemmatization
09 Named Entity Recognition
10 TF-IDF
11 Introduction to Sentiment Analysis
12 Pre-Processing the Dataset
13 Word Embeddings
14 Build the Network
15 Train the Model
16 Test the Model
17 Apply to a Single Input
18 Machine Learning
19 Classification
20 Pre-Processing the Dataset
21 Naïve Bayes and SVM
22 Train the Classifier
23 Test the Classifier
24 Chatbots
25 Simple NLTK Bot
26 Create a ChatterBot
27 Enhancing the Chabot
28 Training the Chabot