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Python Natural Language Processing

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Preface
Chapter 1:Introduction
Understanding natural Ianguage processing
Understanding basic applications
Adantages of togetherness - NLP and Python
Environment setup for NLTK
Tips for readers
Summary

Chapte 2:Practical Understanding of a Corpus and Dataset
What is a corpus?
Why do we need a corpus?
Understanding types of data attributes
Exploring different file formats for corpora
Resources for accessing free corpa
Preparing a dataset for NLP applications
Web scraping
Summary

Chapte 3: Understanding the Structure of a Sentences
Understanding compon3ents of NLP
Natural Ianguage understanding
Defining context-free grammar
Morphological analysis
Syntactic analysis
Semantic analysis
Handling ambiguity
Discourse integration
Pragmatic analysis
Summary

Chapte 4:Preprocessing
Handling corpus-raw text
Handling corpus-raw sentences
Basic preprocessing
Practical and customized preprocessing
Summary

Chapte 5:Feature Engineering and NLP Algorithms
Understanding feature engineering
Basic feature of NLP
Basic statistical features for NLP
Advantages of features engineering
Challenges of features engineering
Summary

Chapte 6:Advanced Feature Engineering and NLP Algorithms
Recall word embedding
Understanding the basice of word2vec
Converting the word2vec model from black box to white box
Undersranding the components of the word2vec model
Understanding the logic of the word2vec model
Understanding algorithmic techniques and the marhemarics vehind the word2vec model
Algorithms used by neural networks
Some of the facts related to word2vec
Applications of word2vec
Implementation of simple examples
Advantages of word2vec
Challenges of word2vec
How is word2vec used in real-life applications?
When should you use word2vec?
Developing something interesting
Extension of the word2vec concept
Importance of vectorization in deep learning
Summary

Chapte 7:Rule-Based Sysrem for NLP
Understanding of the rule-based system
Purpose of having the rule-based system
Archirecrure of the RB system
Understanding the RB system development life cycle Applications
Developing NLP applications using the RB system
Comparing the rule-based approach with other approaches
Advantages of the rule-based system
Disdvantages of the rule-based system
Challenges for the rule-based system
Understanding word-sense disambiguation basics
Discussing recent trends for the rule-based system
Summary

Chapte 8:Machine Learning for NLP Problems
Understanding the basics of machine learning
Development steps for NLP applications
Undersranding ML algorihms and other concepts
Hybrid approaches for NLP applications
Summary

Chapte 9: Deep Learning for NLU and NLG Proclems
An overview of artificial intelligence
Comparing NLU and NLG
A brief overview of deep learning
Basics of neural nerworks
Implementation of ANN
Deep learning and deep neural networks
Deep learning techniques and NLU
Gradient descent-based optimization
Artificial intelligence versus human intelligence
Summary

Chapte 10:Advanced Tools
Apache Hadoop as a storage framework
Apache spark as a processing frmework
Apache Flink as a real-time processing framework
Summary

Chapte 11:How to lmprove Your NLP Skills
Beginning a new career journey with NLP
Cheat sheets
Choose your area
Agile way of working to achieve success
Usful blogs for NLP and data science
Summary

Chapte 12:Insrallation Guide
Installing Python,pip,and NLTK
Installing the PyCharm IDE
Installing dependencies
Framework installation guides
Drop your queries
Summary

Index

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