Artículos relacionados a Natural Language Processing Recipes: Unlocking Text...
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python - Tapa blanda
Sinopsis
Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP.
The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks.
After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.
What You Will Learn
- Know the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and more
- Implement text pre-processing and feature engineering in NLP, including advanced methods of feature engineering
- Understand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learning
Who This Book Is For
Data scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Akshay Kulkarni is an AI and machine learning evangelist and thought leader. He has consulted with Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. Akshay is currently Manager-Data Science & AI at Publicis Sapient where he is part of strategy and transformation interventions through AI. He manages high-priority growth initiatives around data science, works on AI engagements, and applies state-of-the-art techniques. Akshay is a Google Developers Expert-Machine Learning, and is a published author of books on NLP and deep learning. He is a regular speaker at major AI and data science conferences, including Strata, O'Reilly AI Conf, and GIDS. In 2019, he was featured as one of the Top "40 under 40 Data Scientists" in India. In his spare time, he enjoys reading, writing, coding, and helping aspiringdata scientists. He lives in Bangalore with his family.
Adarsha Shivananda is Lead Data Scientist at Indegene's Product and Technology team where he leads a group of analysts who enable predictive analytics and AI features for all of their healthcare software products. They handle multi-channel activities for pharma products and solve real-time problems encountered by pharma sales reps. Adarsha aims to build a pool of exceptional data scientists within the organization and to solve greater health care problems through training programs and staying ahead of the curve. His core expertise involves machine learning, deep learning, recommendation systems, and statistics. Adarsha has worked on data science projects across multiple domains using different technologies and methodologies. Previously, he was part of Tredence Analytics and IQVIA. He lives in Bangalore and loves to read and teach data science.
De la contraportada
Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP.
The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks.
After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.
You will:
- Know the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and more
- Implement text pre-processing and feature engineering in NLP, including advanced methods of feature engineering
- Understand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learning
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialApress
- Año de publicación2021
- ISBN 10 1484273508
- ISBN 13 9781484273500
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición2
- Número de páginas312
- Contacto del fabricanteno disponible
EUR 52,28
EUR 17,52 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 30,88
EUR 8,56 gastos de envío desde Reino Unido a España
Destinos, gastos y plazos de envíoResultados de la búsqueda para Natural Language Processing Recipes: Unlocking Text...
Imagen de archivo
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback / softback. Condición: New. New copy - Usually dispatched within 2 working days. 591. Nº de ref. del artículo: B9781484273500
Cantidad disponible: 2 disponibles
Imagen del vendedor
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python (Paperback or Softback)
Publicado por
Apress 9/27/2021, 2021
ISBN 10: 1484273508
ISBN 13: 9781484273500
Nuevo
Paperback or Softback
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback or Softback. Condición: New. Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python 1.2. Book. Nº de ref. del artículo: BBS-9781484273500
Cantidad disponible: 5 disponibles
Imagen de archivo
Natural Language Processing Recipes
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: S0-9781484273500
Cantidad disponible: 1 disponibles
Imagen del vendedor
Natural Language Processing Recipes
Publicado por
Springer, Berlin|Apress, 2021
ISBN 10: 1484273508
ISBN 13: 9781484273500
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Intermediate user levelFocus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classifica. Nº de ref. del artículo: 486981472
Cantidad disponible: 2 disponibles
Imagen del vendedor
Natural Language Processing Recipes : Unlocking Text Data with Machine Learning and Deep Learning Using Python
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP.The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks.After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.What You Will LearnKnow the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and moreImplement text pre-processing and feature engineering in NLP, including advanced methods of feature engineeringUnderstand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learningWho This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises. Nº de ref. del artículo: 9781484273500
Cantidad disponible: 1 disponibles
Imagen del vendedor
Natural Language Processing Recipes : Unlocking Text Data With Machine Learning and Deep Learning Using Python
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 43169951
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Natural Language Processing Recipes : Unlocking Text Data With Machine Learning and Deep Learning Using Python
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 43169951-n
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Natural Language Processing Recipes : Unlocking Text Data With Machine Learning and Deep Learning Using Python
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 43169951-n
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In English. Nº de ref. del artículo: ria9781484273500_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Natural Language Processing Recipes
Impresión bajo demandaLibrería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP.The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks.After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.What You Will LearnKnow the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and moreImplement text pre-processing and feature engineering in NLP, including advanced methods of feature engineeringUnderstand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learningWho This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises 312 pp. Englisch. Nº de ref. del artículo: 9781484273500
Cantidad disponible: 2 disponibles