Python Text Mining : Perform Text Processing, Word Embedding, Text Classification and Machine Translation

Alexandra George

ISBN 10: 9389898781 / ISBN 13: 9789389898781

Published by BPB Publications, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Natural Language Processing (NLP) has proven to be useful in a wide range of applications. Because of this, extracting information from text data sets requires attention to methods, techniques, and approaches.'Python Text Mining' includes a number of application cases, demonstrations, and approaches that will help you deepen your understanding of feature extraction from data sets. You will get an understanding of good information retrieval, a critical step in accomplishing many machine learning tasks. We will learn to classify text into discrete segments solely on the basis of model properties, not on the basis of user-supplied criteria. The book will walk you through many methodologies, such as classification, that will enable you to rapidly construct recommendation engines, subject segmentation, and sentiment analysis applications. Toward the end, we will also look at machine translation and transfer learning.By the end of this book, you'll know exactly how to gather web-based text, process it, and then apply it to the development of NLP applications.1. Basic Text Processing Techniques2. Text to Numbers3. Word Embeddings4. Topic Modeling5. Unsupervised Sentiment Classification6. Text Classification Using ML7. Text Classification Using Deep learning8. Recommendation engine9. Transfer Learning.

Seller Inventory # 9789389898781

Bibliographic Details

• Title: Python Text Mining : Perform Text Processing, Word Embedding, Text Classification and Machine Translation
• Publisher: BPB Publications
• Publication Date: 2022
• Language: English
• ISBN 10: 9389898781
• ISBN 13: 9789389898781
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 18D millimeters 235 millimeters width by 191 millimeters height by 18 millimeters depth
• Weight: 600 grams

About this title

Synopsis

Make use of the most advanced machine learning techniques to perform NLP and feature extraction

Key Features

● Learn about pre-trained models, deep learning, and transfer learning for NLP applications.

● All-in-one knowledge guide for feature engineering, NLP models, and pre-processing techniques.

● Includes use cases, enterprise deployments, and a range of Python based demonstrations.

Description

Natural Language Processing (NLP) has proven to be useful in a wide range of applications. Because of this, extracting information from text data sets requires attention to methods, techniques, and approaches.

'Python Text Mining' includes a number of application cases, demonstrations, and approaches that will help you deepen your understanding of feature extraction from data sets. You will get an understanding of good information retrieval, a critical step in accomplishing many machine learning tasks. We will learn to classify text into discrete segments solely on the basis of model properties, not on the basis of user-supplied criteria. The book will walk you through many methodologies, such as classification, that will enable you to rapidly construct recommendation engines, subject segmentation, and sentiment analysis applications. Toward the end, we will also look at machine translation and transfer learning.

By the end of this book, you'll know exactly how to gather web-based text, process it, and then apply it to the development of NLP applications.

What you will learn

● Practice how to process raw data and transform it into a usable format.

● Best techniques to convert text to vectors and then transform into word embeddings.

● Unleash ML and DL techniques to perform sentiment analysis.

● Build modern recommendation engines using classification techniques.

Who this book is for

This book is a good place to start with examples, explanations, and exercises for anyone interested in learning more about advanced text mining and natural language processing techniques. It is suggested but not required that you have some prior programming experience.

Table of Contents

1. Basic Text Processing Techniques

2. Text to Numbers

3. Word Embeddings

4. Topic Modeling

5. Unsupervised Sentiment Classification

6. Text Classification Using ML

7. Text Classification Using Deep learning

8. Recommendation engine

9. Transfer Learning