Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applicati

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Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applicati

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內容描述

Key Features

Perform various NLP tasks to build linguistic applications using Python libraries
Understand, analyze, and generate text to provide accurate results
Interpret human language using various NLP concepts, methodologies, and tools

Book Description
Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding.
This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you'll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own.
By the end of this NLP book, you'll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
What you will learn

Understand how NLP powers modern applications
Explore key NLP techniques to build your natural language vocabulary
Transform text data into mathematical data structures and learn how to improve text mining models
Discover how various neural network architectures work with natural language data
Get the hang of building sophisticated text processing models using machine learning and deep learning
Check out state-of-the-art architectures that have revolutionized research in the NLP domain

Who this book is for
This NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.

目錄大綱

Understanding the Basics of NLP
NLP Using Python
Building your NLP Vocabulary
Transforming Text into Data Structures
Word Embeddings and Distance Measurements for Text
Exploring Sentence-, Document-, and Character-Level Embeddings
Identifying Patterns in Text using Machine Learning
From Human Neurons to Artificial Neurons for Understanding Text
Applying Convolutions to Text
Capturing Temportal Relationships in Text
State of the Art in NLP

作者介紹

Aman Kedia is a data enthusiast and lifelong learner. He is an avid believer in Artificial Intelligence (AI) and the algorithms supporting it. He has worked on state-of-the-art problems in Natural Language Processing (NLP), encompassing resume matching and digital assistants, among others. He has worked at Oracle and SAP, trying to solve problems leveraging advancements in AI. He has four published research papers in the domain of AI.
Mayank Rasu has more than 12 years of global experience as a data scientist and quantitative analyst in the investment banking industry. He has worked at the intersection of finance and technology and has developed and deployed AI-based applications within the finance domain. His experience includes building sentiment analyzers, robotics, and deep learning-based document review, among many others areas.