Natural Language Processing (NLP) Opplæringskurs

Natural Language Processing (NLP) Opplæringskurs

Local, instructorled live Natural Language Process (NLP) training courses demonstrate through interactive discussion and handson practice how to extract insights and meaning from this data Utilizing different programming languages such as Python and R and Natural Language Processing (NLP) libraries, our trainings combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to help participants understand the meaning behind text data NLP trainings walk participants stepbystep through the process of evaluating and applying the right algorithms to analyze data and report on its significance

NLP training is available as "onsite live training" or "remote live training" Onsite live training can be carried out locally on customer premises in Norge or in NobleProg corporate training centers in Norge Remote live training is carried out by way of an interactive, remote desktop

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NLP (Natural Language Processing) Underkategorier

Natural Language Processing Kursplaner

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Oversikt
Kursnavn
Varighet
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7 timer
Oversikt
Dette kurset er laget for ledere, løsningsarkitekter, innovasjonsansvarlige, CTOer, programvarearkitekter og alle som er interessert i en oversikt over anvendt kunstig intelligens og den nærmeste prognosen for dens utvikling.
21 timer
Oversikt
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 timer
Oversikt
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.
28 timer
Oversikt
DL (Deep Learning) is a subset of ML (Machine Learning).

Python is a popular programming language that contains libraries for Deep Learning for NLP.

Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos.

In this instructor-led, live training, 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.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
21 timer
Oversikt
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
21 timer
Oversikt
Dette kurset er designet for folk som er interessert i å hente ut mening fra skrevet engelsk tekst, selv om kunnskapen også kan brukes på andre menneskelige språk.

Kurset vil dekke hvordan man bruker tekst skrevet av mennesker, for eksempel blogginnlegg, tweets, osv. ...

For eksempel kan en analytiker sette opp en algoritme som automatisk kommer til en konklusjon basert på omfattende datakilde.
21 timer
Oversikt
Det anslås at ustrukturerte data står for mer enn 90 prosent av alle data, mye av det i form av tekst. Blogginnlegg, tweets, sosiale medier og andre digitale publikasjoner bidrar kontinuerlig til denne voksende datamaskinen.

Dette instruktørledede, levende kurset fokuserer på å trekke ut innsikt og mening fra disse dataene. Ved å bruke R Language and Natural Language Processing (NLP) -bibliotekene kombinerer vi konsepter og teknikker fra informatikk, kunstig intelligens og beregningsspråklighet for å algoritmisk forstå betydningen bak tekstdata. Dataprøver er tilgjengelig på forskjellige språk per kundebehov.

Mot slutten av denne opplæringen vil deltakerne kunne utarbeide datasett (store og små) fra forskjellige kilder, og deretter bruke de rette algoritmene for å analysere og rapportere om betydningen av den.

Kursets format

- Delforelesning, deldiskusjon, tung praktisk praksis, sporadiske tester for å måle forståelse
21 timer
Oversikt
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 timer
Oversikt
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
21 timer
Oversikt
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
35 timer
Oversikt
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.
28 timer
Oversikt
Dette kurset introduserer lingvister eller programmerere til NLP i Python . I løpet av dette kurset vil vi stort sett bruke nltk.org (Natural Language Tool Kit), men også vi vil bruke andre biblioteker som er relevante og nyttige for NLP. For øyeblikket kan vi gjennomføre dette kurset i Python 2.x eller Python 3.x. Eksempler er på engelsk eller mandarin (普通话). Andre språk kan også gjøres tilgjengelig hvis avtalt før bestilling.
14 timer
Oversikt
Denne instruktørledede, liveopplæringen (stedet eller fjernkontrollen) er rettet mot utviklere og dataforskere som ønsker å bruke spaCy til å behandle veldig store mengder tekst for å finne mønstre og få innsikt.

Ved slutten av denne opplæringen vil deltakerne kunne:

- Installer og konfigurer spaCy.
- Forstå spaCys tilnærming til Natural Language Processing (NLP) .
- Pakk ut mønstre og få forretningsinnsikt fra store datakilder.
- Integrer spaCy-biblioteket med eksisterende nett- og eldre applikasjoner.
- Distribuer spaCy til levende produksjonsmiljøer for å forutsi menneskelig atferd.
- Bruk spaCy til å forarbeide tekst for Deep Learning

Kursets format

- Interaktiv forelesning og diskusjon.
- Masse øvelser og trening.
- Praktisk implementering i et live-lab-miljø.

Alternativer for tilpasning av kurset

- For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å avtale.
- Hvis du vil lære mer om spaCy, kan du gå til: https://spacy.io/
14 timer
Oversikt
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
35 timer
Oversikt
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
14 timer
Oversikt
Deeplearning4j er et open source, distribuert Deeplearning4j skrevet for Java og Scala . Integrert med Hadoop og Spark, er DL4J designet for å brukes i forretningsmiljøer på distribuerte GPU er og CPU-er.

Word 2Vec er en metode for å beregne vektorrepresentasjoner av ord introdusert av et team av forskere ved Go ogle ledet av Tomas Mikolov.

Publikum

Dette kurset er rettet mot forskere, ingeniører og utviklere som søker å bruke Deeplearning4J til å konstruere Word 2Vec-modeller.
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