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

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

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

Stu Arnold und Frank Lemmerz sind bereits seit eh und je beste Freunde. Als sich die Schulzeit dem Ende nähert und sie sich zwischen Bundeswehr oder Zivildienst entscheiden müssen, beschließen sie den Weg des geringsten Übels einzuschlagen. Als "Jugendherbergszivi" wollen sie das eine Jahr, welches sie für Vater Staat ableisten müssen, in der Jugendherberge verbringen.Dort treffen sie auf die schrägen Herbergseltern Schröder, die mit ihrer sehr eigenen Weltanschauung die Herberge seit Jahrzehnten leiten. Sie kollidieren mit dem nörgelnden Rezeptionisten Herrn Schwilgerer, und der andauernd kontrollierenden Küchenangestellten Frau Kuftel. Zwischen den Festangestellten und weiteren Zivildienstleistenden kommt es tagsüber zu so manchem Machtkampf.Nachts kommt es, zu vorgerückter Stunde, Dank der Schlosserausbildung eines Zivis und des nachgemachten Schlüssels für das Weinlager der Herberge, zu mancher exzessiven Feier. Frank, der aus wohlbetuchtem Elternhaus kommt, fällt es zunächst schwer sich bei der Arbeit selbst die Hände schmutzig zu machen.Stu versucht so viele Bekanntschaften wie möglich zu machen und gerät in einen Strudel aus Liebeskummer.... "Der Jugendherbergszivi" ist eine Hommage an den Zivildienst. Jede Situation ist 100% zum Fremdschämen und ein Angriff auf die Lachmuskeln.

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

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

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

Stu Arnold und Frank Lemmerz sind bereits seit eh und je beste Freunde. Als sich die Schulzeit dem Ende nähert und sie sich zwischen Bundeswehr oder Zivildienst entscheiden müssen, beschließen sie den Weg des geringsten Übels einzuschlagen. Als "Jugendherbergszivi" wollen sie das eine Jahr, welches sie für Vater Staat ableisten müssen, in der Jugendherberge verbringen.Dort treffen sie auf die schrägen Herbergseltern Schröder, die mit ihrer sehr eigenen Weltanschauung die Herberge seit Jahrzehnten leiten. Sie kollidieren mit dem nörgelnden Rezeptionisten Herrn Schwilgerer, und der andauernd kontrollierenden Küchenangestellten Frau Kuftel. Zwischen den Festangestellten und weiteren Zivildienstleistenden kommt es tagsüber zu so manchem Machtkampf.Nachts kommt es, zu vorgerückter Stunde, Dank der Schlosserausbildung eines Zivis und des nachgemachten Schlüssels für das Weinlager der Herberge, zu mancher exzessiven Feier. Frank, der aus wohlbetuchtem Elternhaus kommt, fällt es zunächst schwer sich bei der Arbeit selbst die Hände schmutzig zu machen.Stu versucht so viele Bekanntschaften wie möglich zu machen und gerät in einen Strudel aus Liebeskummer.... "Der Jugendherbergszivi" ist eine Hommage an den Zivildienst. Jede Situation ist 100% zum Fremdschämen und ein Angriff auf die Lachmuskeln.