Lokale, instruktorierte Live-Trainingsseminare für Natural Language Process (NLP) demonstrieren durch interaktive Diskussionen und praktische Übungen, wie aus diesen Daten Einsichten und Bedeutung gewonnen werden können Mit verschiedenen Programmiersprachen wie Python und R und Natural Language Processing (NLP) -Bibliotheken kombinieren unsere Trainings Konzepte und Techniken aus den Bereichen Informatik, künstliche Intelligenz und Computerlinguistik, um den Teilnehmern zu helfen, die Bedeutung von Textdaten zu verstehen NLP-Schulungen führen die Teilnehmer durch den Prozess der Auswertung und Anwendung der richtigen Algorithmen, um Daten zu analysieren und über ihre Bedeutung zu berichten NLP-Training ist als "Live-Training vor Ort" oder "Fern-Live-Training" verfügbar Onsite Live Training kann vor Ort bei Kunden durchgeführt werden Schweiz oder in NobleProg Corporate Trainingszentren in Schweiz Remote-Live-Training wird über einen interaktiven Remote-Desktop durchgeführt NobleProg Ihr lokaler Trainingsanbieter.
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Erfahrungsberichte
Ich mochte die Übungen.
Office for National Statistics
Kurs: Natural Language Processing with Python
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Der Trainer erklärte sehr leicht schwierige und fortgeschrittene Themen.
Leszek K
Kurs: Artificial Intelligence Overview
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Dies ist einer der besten praktischen Übungen, die ich je gemacht habe.
Laura Kahn
Kurs: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Dies ist eines der besten Online-Trainings, die ich je in meiner 13-jährigen Karriere gemacht habe. Mach weiter so!
Kurs: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Personalausweis und Leiterplattenminderkennung
王 春柱 - 中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
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beweisen
中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
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Über Gesichtsbereich.
中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
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der letzte Tag. Generation Teil
Accenture Inc
Kurs: Python for Natural Language Generation
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Die Themen beziehen sich auf NLG. Das Team konnte am Ende etwas Neues mit interessanten Themen lernen, aber es war erst am letzten Tag. Es gab auch mehr praktische Aktivitäten als Folien, was gut war.
Accenture Inc
Kurs: Python for Natural Language Generation
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Mir gefällt, dass es mehr um das How-to der verschiedenen Methoden der Textzusammenfassung geht
Kurs: Text Summarization with Python
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Themen, freundliche Haltung des Moderators
Fujitsu Technology Solutions Sp. z o.o.
Kurs: Artificial Intelligence Overview
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Dies ist eines der besten Online-Trainings, die ich je in meiner 13-jährigen Karriere gemacht habe. Mach weiter so!
Kurs: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Mir gefällt, dass es mehr um das How-to der verschiedenen Methoden der Textzusammenfassung geht
Kurs: Text Summarization with Python
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NLP Unterkategorien
NLP Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
7 Stunden
Überblick
Dieser Kurs richtet sich an Manager, Lösungsarchitekten, Innovationsbeauftragte, CTOs, Softwarearchitekten und alle, die sich für einen Überblick über angewandte künstliche Intelligenz und die nächste Prognose für deren Entwicklung interessieren.
21 Stunden
Überblick
Dieser Kurs richtet sich an Personen, die daran interessiert sind, dem geschriebenen englischen Text Bedeutung zu entziehen, obwohl das Wissen auch auf andere menschliche Sprachen übertragen werden kann.
Der Kurs befasst sich mit der Verwendung von Texten, die von Menschen geschrieben wurden, z. B. Blog-Posts, Tweets usw.
Beispielsweise kann ein Analyst einen Algorithmus einrichten, der auf der Grundlage einer umfangreichen Datenquelle automatisch zu einer Schlussfolgerung gelangt.
Der Kurs befasst sich mit der Verwendung von Texten, die von Menschen geschrieben wurden, z. B. Blog-Posts, Tweets usw.
Beispielsweise kann ein Analyst einen Algorithmus einrichten, der auf der Grundlage einer umfangreichen Datenquelle automatisch zu einer Schlussfolgerung gelangt.
28 Stunden
Überblick
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
35 Stunden
Überblick
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
14 Stunden
Überblick
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
21 Stunden
Überblick
This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
21 Stunden
Überblick
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
21 Stunden
Überblick
In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 Stunden
Überblick
Natural language generation (NLG) refers to the production of natural language text or speech by a computer.
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
35 Stunden
Überblick
By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.
14 Stunden
Überblick
The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 Stunden
Überblick
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 Stunden
Überblick
In this instructor-led, live training in Schweiz, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
- Design and code DL for NLP using Python libraries.
- Create Python code that reads a substantially huge collection of pictures and generates keywords.
- Create Python Code that generates captions from the detected keywords.
By the end of this training, participants will be able to:
- Design and code DL for NLP using Python libraries.
- Create Python code that reads a substantially huge collection of pictures and generates keywords.
- Create Python Code that generates captions from the detected keywords.
21 Stunden
Überblick
This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python
21 Stunden
Überblick
ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions.
In this instructor-led, live training, participants will learn how to build chatbots in Python.
By the end of this training, participants will be able to:
- Understand the fundamentals of building chatbots
- Build, test, deploy, and troubleshoot various chatbots using Python
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training, participants will learn how to build chatbots in Python.
By the end of this training, participants will be able to:
- Understand the fundamentals of building chatbots
- Build, test, deploy, and troubleshoot various chatbots using Python
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
14 Stunden
Überblick
This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights.
By the end of this training, participants will be able to:
- Install and configure spaCy.
- Understand spaCy's approach to Natural Language Processing (NLP).
- Extract patterns and obtain business insights from large-scale data sources.
- Integrate the spaCy library with existing web and legacy applications.
- Deploy spaCy to live production environments to predict human behavior.
- Use spaCy to pre-process text for Deep Learning
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about spaCy, please visit: https://spacy.io/
By the end of this training, participants will be able to:
- Install and configure spaCy.
- Understand spaCy's approach to Natural Language Processing (NLP).
- Extract patterns and obtain business insights from large-scale data sources.
- Integrate the spaCy library with existing web and legacy applications.
- Deploy spaCy to live production environments to predict human behavior.
- Use spaCy to pre-process text for Deep Learning
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about spaCy, please visit: https://spacy.io/
Zukünftige NLP (Natural Language Processing) Kurse
22-11-2021 09:30
28 hours
30-11-2021 09:30
21 hours
17-12-2021 09:30
7 hours
17-12-2021 09:30
7 hours
20-12-2021 09:30
35 hours
Andere Länder
Consulting
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