Local instructor-led live Natural Language Processing (NLP) training courses in България.
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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
Име на Kурса
Продължителност
Общ преглед
Име на Kурса
Продължителност
Общ преглед
14 hours
Generative Pre-trained Transformers (GPT) са най-съвременни модели в обработката на естествен език, които революционизират различни приложения, включително генериране на език, довършване на текст и машинен превод. Този курс предоставя задълбочено изследване на GPT моделите, с акцент върху GPT-3 и най-новите постижения в GPT-4. Участниците ще придобият представа за архитектурата, техниките за обучение и приложенията на GPT моделите.Това водено от инструктор обучение на живо (онлайн или на място) е насочено към специалисти по данни, инженери по машинно обучение, NLP изследователи и AI ентусиасти, които желаят да разберат вътрешната работа на GPT моделите, да изследват възможностите на GPT-3 и GPT-4 , и научете как да използвате тези модели за своите NLP задачи.До края на това обучение участниците ще могат:
- Разберете ключовите концепции и принципи зад Generative Pre-trained Transformers. Разберете архитектурата и процеса на обучение на GPT модели. Използвайте GPT-3 за задачи като генериране на текст, допълване и превод. Разгледайте най-новите постижения в GPT-4 и неговите потенциални приложения. Прилагат GPT модели към собствените си НЛП проекти и задачи.
- Интерактивна лекция и дискусия. Много упражнения и практика. Практическо внедряване в лабораторна среда на живо.
- За да поискате персонализирано обучение за този курс, моля свържете се с нас, за да уговорим.
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.
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
28 hours
Този курс въвежда лингвисти или програмисти за NLP в Python. По време на този курс ще използваме най-вече nltk.org (Natural Language Tool Kit), но също така ще използваме други библиотеки, които са релевантни и полезни за NLP. В момента можем да извършим този курс в Python 2.x или Python 3.x. Примери са на английски или мандарински (普通话). Други езици също могат да бъдат предоставени, ако са договорени преди резервацията.
7 hours
Този курс е създаден за мениджъри, архитекти на решения, офицери за иновации, ЦТО, софтуерни архитекти и всеки, който се интересува от преглед на приложното изкуствено разузнаване и най-близката прогноза за неговото развитие.
35 hours
TensorFlow™ е софтуерна библиотека с отворен код за цифрови изчисления, използвайки графики за потока на данни.
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
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.
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
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
- Developers
- Data Scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
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
- Developers
- Data scientists
- 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
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
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
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
- Developers
- Data Scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
In this instructor-led, live training in България, 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.
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
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
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
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
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
- 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
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.
14 hours
This instructor-led, live training in България (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.).
14 hours
This instructor-led, live training in България (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.
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