Les cours de formation sur les processus de langage naturel (PNL) en direct, animés par un instructeur, démontrent par des discussions interactives et des exercices pratiques comment extraire les idées et la signification de ces données Utilisant différents langages de programmation tels que les bibliothèques Python et R et Natural Language Processing (NLP), nos formations combinent des concepts et des techniques de l'informatique, de l'intelligence artificielle et de la linguistique computationnelle pour aider les participants à comprendre la signification des données textuelles Les formations en PNL aident les participants à franchir le processus d'évaluation et d'application des bons algorithmes pour analyser les données et rendre compte de leur importance La formation en PNL est disponible en tant que «formation en direct sur site» ou «formation en direct à distance» La formation en direct sur site peut être effectuée localement dans les locaux du client Belgique ou dans les centres de formation d'entreprise NobleProg à Belgique La formation en ligne à distance est réalisée au moyen d'un ordinateur de bureau interactif et distant NobleProg Votre fournisseur de formation local.
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Formation: Natural Language Processing with Python
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Formation: Artificial Intelligence Overview
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Il s’agit de l’un des meilleurs exercices pratiques de programmation que je connaisse.
Laura Kahn
Formation: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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C’est l’une des meilleures formations en ligne de qualité que j’ai jamais eues au cours de ma carrière de 13 ans. Continuez ce bon travail!.
Formation: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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王 春柱 - 中移物联网
Formation: Deep Learning for NLP (Natural Language Processing)
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Formation: Deep Learning for NLP (Natural Language Processing)
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Formation: Deep Learning for NLP (Natural Language Processing)
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le dernier jour. partie génération
Accenture Inc
Formation: Python for Natural Language Generation
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Les sujets faisant référence à NLG. L'équipe a finalement pu apprendre quelque chose de nouveau avec des sujets intéressants, mais ce n'est que le dernier jour. Il y avait aussi plus d'activités pratiques que de diapositives, ce qui était bien.
Accenture Inc
Formation: Python for Natural Language Generation
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J'aime le fait qu'il se concentre davantage sur le comment faire des différentes méthodes de résumé de texte
Formation: Text Summarization with Python
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Fujitsu Technology Solutions Sp. z o.o.
Formation: Artificial Intelligence Overview
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C’est l’une des meilleures formations en ligne de qualité que j’ai jamais eues au cours de ma carrière de 13 ans. Continuez ce bon travail!.
Formation: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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J'aime le fait qu'il se concentre davantage sur le comment faire des différentes méthodes de résumé de texte
Formation: Text Summarization with Python
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Sous-catégories Natural Language Processing
Plans de cours Natural Language Processing
Nom du Cours
Durée
Aperçu
Nom du Cours
Durée
Aperçu
28 hours
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.
7 hours
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
21 hours
This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
35 hours
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 hours
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 hours
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 hours
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
14 hours
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
28 hours
In this instructor-led, live training in Belgique, 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 hours
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
14 hours
This instructor-led, live training in Belgique (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building NLP pipelines with Spark NLP.
- Understand the features, architecture, and benefits of using Spark NLP.
- Use the pre-trained models available in Spark NLP to implement text processing.
- Learn how to build, train, and scale Spark NLP models for production-grade projects.
- Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
21 hours
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 hours
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 hours
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
14 hours
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/
21 hours
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.
21 hours
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
14 hours
This instructor-led, live training in Belgique (online or onsite) is aimed at data scientists and developers who wish to use TextBlob to implement and simplify NLP tasks, such as sentiment analysis, spelling corrections, text classification modeling, etc.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start implementing NLP tasks with TextBlob.
- Understand the features, architecture, and advantages of TextBlob.
- Learn how to build text classification systems using TextBlob.
- Perform common NLP tasks (Tokenization, WordNet, Sentiment analysis, Spelling correction, etc.)
- Execute advanced implementations with simple APIs and a few lines of codes.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start implementing NLP tasks with TextBlob.
- Understand the features, architecture, and advantages of TextBlob.
- Learn how to build text classification systems using TextBlob.
- Perform common NLP tasks (Tokenization, WordNet, Sentiment analysis, Spelling correction, etc.)
- Execute advanced implementations with simple APIs and a few lines of codes.
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Prochains cours NLP
Natural Language Processing with Python
14-02-2022 09:30
28 heures
NLP with Deeplearning4j
28-02-2022 09:30
14 heures
Natural Language Processing
02-03-2022 09:30
21 heures
Natural Language Processing with TensorFlow
07-03-2022 09:30
35 heures
Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
15-03-2022 09:30
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