ISBN: 9781492074083 - blueprints for text analytics using python: machine learning based solutions for common real world (nlp) applications (30 resultados)

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Paperback. Condición: New. Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.Extract data from APIs and web pagesPrepare textual data for statistical analysis and machine learningUse machine learning for classification, topic modeling, and summarizationExplain AI models and classification resultsExplore and visualize semantic similarities with word embeddingsIdentify customer sentiment in product reviewsCreate a knowledge graph based on named entities and their relations.…

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Paperback. Condición: New. Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.Extract data from APIs and web pagesPrepare textual data for statistical analysis and machine learningUse machine learning for classification, topic modeling, and summarizationExplain AI models and classification resultsExplore and visualize semantic similarities with word embeddingsIdentify customer sentiment in product reviewsCreate a knowledge graph based on named entities and their relations.…

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Paperback. Condición: new. Paperback. Turning text into valuable information is essential for many businesses looking to gain a competitive advantage. There have many improvements in natural language processing and users have a lot of options when choosing to work on a problem. However, it's not always clear which NLP tools or libraries would work for a business use--or which techniques you should use and in what order. This practical book provides theoretical background and real-world case studies with detailed code examples to help developers and data scientists obtain insight from text online. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler use blueprints for text-related problems that apply state-of-the-art machine learning methods in Python. If you have a fundamental understanding of statistics and machine learning along with basic programming experience in Python, you're ready to get started. You'll learn how to: Crawl and clean then explore and visualize textual data in different formatsPreprocess and vectorize text for machine learningApply methods for classification, topic analysis, summarization, and knowledge extractionUse semantic word embeddings and deep learning approaches for complex problemsWork with Python NLP libraries like spaCy, NLTK, and Gensim in combination with scikit-learn, Pandas, and PyTorch This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Condición: New. 2020. Paperback. . . . . .

Condición: New. pp. 350.

Paperback. Condición: New. Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.Extract data from APIs and web pagesPrepare textual data for statistical analysis and machine learningUse machine learning for classification, topic modeling, and summarizationExplain AI models and classification resultsExplore and visualize semantic similarities with word embeddingsIdentify customer sentiment in product reviewsCreate a knowledge graph based on named entities and their relations.…

Paperback. Condición: new. Paperback. Turning text into valuable information is essential for many businesses looking to gain a competitive advantage. There have many improvements in natural language processing and users have a lot of options when choosing to work on a problem. However, it's not always clear which NLP tools or libraries would work for a business use--or which techniques you should use and in what order. This practical book provides theoretical background and real-world case studies with detailed code examples to help developers and data scientists obtain insight from text online. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler use blueprints for text-related problems that apply state-of-the-art machine learning methods in Python. If you have a fundamental understanding of statistics and machine learning along with basic programming experience in Python, you're ready to get started. You'll learn how to: Crawl and clean then explore and visualize textual data in different formatsPreprocess and vectorize text for machine learningApply methods for classification, topic analysis, summarization, and knowledge extractionUse semantic word embeddings and deep learning approaches for complex problemsWork with Python NLP libraries like spaCy, NLTK, and Gensim in combination with scikit-learn, Pandas, and PyTorch This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world.

Condición: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Condición: Neu. Neuware - Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.- Extract data from APIs and web pages- Prepare textual data for statistical analysis and machine learning- Use machine learning for classification, topic modeling, and summarization- Explain AI models and classification results- Explore and visualize semantic similarities with word embeddings- Identify customer sentiment in product reviews- Create a knowledge graph based on named entities and their relations.…

Paperback. Condición: New. Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.Extract data from APIs and web pagesPrepare textual data for statistical analysis and machine learningUse machine learning for classification, topic modeling, and summarizationExplain AI models and classification resultsExplore and visualize semantic similarities with word embeddingsIdentify customer sentiment in product reviewsCreate a knowledge graph based on named entities and their relations.…

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