
Local instructor-led live Natural Language Processing (NLP) training courses in България.
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Fujitsu Technology Solutions Sp. z o.o.
Course: Artificial Intelligence Overview
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информация
Amr Alaa - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Изучаване на нов език.
FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Технически знания за знание през зна
Aly Saleh - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Полезен и добър слушател .. Интерактивен
Ahmed El Kholy - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Ахмед беше много интерактивен и нямаше нищо против да отговори на всякакъв вид добре представяне и гладък поток от курса
Mohamed Ghowaiba - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Курсът е много интересен да бъдеш основният фокус на ден
mohamed taher - FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Дискусиите за разширяване на нашите хоризонти
FAB banak Egypt
Course: Introduction to Data Science and AI (using Python)
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Natural Language Processing (NLP) Subcategories
Natural Language Processing (NLP) Course Outlines
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
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.
SyntaxNet е рамка за обработка на естествени езици с невронна мрежа за TensorFlow.
Word2Vec се използва за изучаване на векторни представи на думи, наречени "word embeddings". Word2vec е специално изчислително-ефективна предсказуема модел за изучаване на въвеждането на думи от суров текст. Той идва в два вкуса, моделът Continuous Bag-of-Words (CBOW) и моделът Skip-Gram (глави 3.1 и 3.2 в Mikolov et al.)
Използвани в тандем, SyntaxNet и Word2Vec позволяват на потребителите да генерират модели за учене от естествения език.
публиката
Този курс е насочен към разработчици и инженери, които възнамеряват да работят с SyntaxNet и Word2Vec модели в техните TensorFlow графики.
След завършване на този курс делегатите ще:
Разбиране на структурата и механизмите за разпространение на TensorFlow’ да може да изпълнява монтаж / производствена среда / архитектурни задачи и конфигурация да могат да оценяват качеството на кода, да извършват дебютиране, мониторинг да могат да прилагат напреднали производствени модели като модели за обучение, термини за вграждане, графика за строителство и записване
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.
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
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 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 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
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.
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.
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:
- 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 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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