Hands-on natural language processing with python:pdf free download

This book covers the following exciting features: 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 If you feel this book is for you, get your copy today!

Instructions and Navigations All of the code is organized into folders. About No description or website provided. Releases No releases published. Packages 0 No packages published. Contributors 5. You signed in with another tab or window. Reload to refresh your session.

You signed out in another tab or window. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.

Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. This book focuses on how natural language processing NLP is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python.

Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them.

You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on.

By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book. Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to Published on : April 9, Python version: TH pages. Downey 0. If you want to learn how to program, working with Python is an excellent way to start.

This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts Published on : Dec. Begin by building classic games like Hangman, Guess the Number, and Tic-Tac-Toe, and then work your way up to more advanced games, like a text-based treasure hunting game and an animated collision Learn how to program in Python while making and breaking ciphers—algorithms used to create and send secret messages!

Published on : Jan. The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. Pronoun resolution resolves the pronouns in a text when there are several people interacting. Searching websites for information is an integral part of accessing the internet, and is an application of NLP. For question answering systems, a context is supplied with a question in order to generate an answer.

Sometimes, a text has to be converted into a voice. It can be useful for a personal bot to speak back to a user. With the API, you can pass the text and convert it to speech. The audio file can be either streamed or downloaded. The voice should be natural sounding, to connect with the user. Google can provide this in 30 different voices, in 12 different languages. The speed and pitch can be adjusted.

The following screenshot shows an example; all of the parameters can be tuned:. The requests can be sent from any connected device, such as a mobile, car, TV, and so on.

It can be used for customer service, presenting educational text, or for animation content. Sometimes, a voice has to be converted to text. This is a speech recognition problem. The Google speech recognition system works in languages. The audio can be streamed, or a prerecorded video can be sent.

Formatting can be done for different categories, such as proper nouns and punctuation. There are different models provided, for videos, phone calls, and search-based audio. This works even when there is background noise, and the system can filter inappropriate content. Speaker identification is the task of finding the name of the person that is speaking.

All of the applications, such as chatbots, voice-to-text, text-to-voice, speaker identification, and searching, can be combined to form the experience of spoken dialog systems. In this chapter, you learned the basics of NLP and saw several applications where it can be useful. Having seen the cloud products, you will learn the science behind the applications in future chapters.

In the next chapter, we will look at the basics of the NLTK library. We will cover basic feature engineering and will program a simple task for text classification. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore.

He has published papers in conferences and has pending patents in storage and ML. Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

Gain knowledge of various deep neural network architectures and their areas of application to conquer your NLP issues. About this book Natural language processing NLP has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Publication date: July Publisher Packt.

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