Training: Aan de slag met Natural Language Processing
Natural Language Processing
14 uur
Engels (US)

Training: Aan de slag met Natural Language Processing

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Productinformatie

In deze training krijg jij de kennis en vaardigheden om grote hoeveelheden language data te analyseren die door bedrijven over de hele wereld zijn gegenereerd. Deze language data omvatten verschillende soorten documenten, rapporten, e-mails en juridische inhoud. Je begint deze training met het leren van de basis van natural language processing (NLP) door de fundamentele bouwstenen en technieken te verkennen die worden gebruikt om waardevolle inzichten uit deze gegevens te halen. Vervolgens duik je in taalkundige kenmerken zoals corpora, tokenization, stemming, lemmatization en de betekenis van stopwoorden in NLP. Leer meer over de praktische toepassingen en sterke punten van verschillende NLP-tools, zoals NLTK, spaCy, polyglot, Gensim, TextBlob en CoreNLP. Je zal ook ontdekken hoe je WordNet kan gebruiken voor het extraheren van synoniemen en hyperniemen.

Machine learning speelt een cruciale rol in NLP, en de training gaat dieper in op ML-pijplijnen en algemene modellen die worden gebruikt bij het oplossen van NLP-problemen. Je krijgt praktijkvoorbeeld van het identificeren van sarcasme in tekst en bespreekt geschikte machine learning-technieken. Ten slotte doe je praktische ervaring op met het implementeren van belangrijke taalkundige functies zoals POS-tagging, named entity recognition (NER) en morfologische analyse.

Inhoud van de training

Aan de slag met Natural Language Processing

14 uur

Natural Language Processing: Getting Started with NLP

Enterprises across the world are creating large amounts of language data. There are many different kinds of data with language components including reports, word documents, operational data, emails, reviews, sops, and legal documents. This course will help you develop the skills to analyze this data and extract valuable and actionable insights. Learn about the various building blocks of natural language processing to help in understanding the different approaches used for solving NLP problems. Examine machine learning and deep learning approaches to handling NLP issues. Finally, explore common use cases that companies are approaching with NLP solutions. Upon completion of this course, you will have a strong foundation in the fundamentals of natural language processing, its building blocks, and the various approaches that can be used to architect solutions for enterprises in NLP domains.

Natural Language Processing: Linguistic Features Using NLTK & spaCy

Without fundamental building blocks and industry-accepted tools, it is difficult to achieve state-of-art analysis in NLP. In this course, you will learn about linguistic features such as word corpora, tokenization, stemming, lemmatization, and stop words and understand their value in natural language processing. Begin by exploring NLTK and spaCy, two of the most widely used NLP tools, and understand what they can help you achieve. Learn to recognize the difference between these tools and understand the pros and cons of each. Discover how to implement concepts like part of speech tagging, named entity recognition, dependency parsing, n-grams, spell correction, segmenting sentences, and finding similar sentences. Upon completion of this course, you will be able to build basic NLP applications on any raw language data and explore the NLP features that can help businesses take actionable steps with this data.

Text Mining and Analytics: Pattern Matching & Information Extraction

Sometimes, business wants to find similar-sounding words, specific word occurrences, and sentiment from the raw text. Having learned to extract foundational linguistic features from the text, the next objective is to learn the heuristic approach to extract non-foundational features which are subjective. In this course, learn how to extract synonyms and hypernyms with WordNet, a widely used tool from the Natural Language Toolkit (NLTK). Next, explore the regex module in Python to perform NLTK chunking and to extract specific required patterns. Finally, you will solve a real-world use case by finding sentiments of movies using WordNet. After comleting this course, you will be able to use a heuristic approach of natural language processing (NLP) and to illustrate the use of WordNet, NLTK chunking, regex, and SentiWordNet.

Text Mining and Analytics: Machine Learning for Natural Language Processing

Machine learning (ML) is one of the most important toolsets available in the enterprise world. It gives predictive powers to data that can be leveraged to investigate future behaviors and patterns. It can help companies proactively improve their business and help optimize their revenue. Learn how to leverage machine learning to make predictions with language data. Explore the ML pipelines and common models used for Natural Language Processing (NLP). Examine a real-world use case of identifying sarcasm in text and discover the machine learning techniques suitable for NLP problems. Learn different vectorization and feature engineering methods for text data, exploratory data analysis for text, model building, and evaluation for predicting from text data and how to tune those models to achieve better results. After completing this course, you'll be able to illustrate the use of machine learning to solve NLP problems and demonstrate the use of NLP feature engineering.

Text Mining and Analytics: Natural Language Processing Libraries

There are many tools available in the Natural Language Processing (NLP) tool landscape. With single tools, you can do a lot of things faster. However, using multiple state-of-art tools together, you can solve many problems and extract multiple patterns from your data. In this course, you will discover many important tools available for NLP such as polyglot, Genism, TextBlob, and CoreNLP. Explore their benefits and how they stand against each other for performing any NLP task. Learn to implement core linguistic features like POS tags, NER, and morphological analysis using the tools discussed earlier in the course. Discover defining features of each tool such as multiple language support, language detection, topic models, sentiment extractions, part of speech (POS) driven patterns, and transliterations. Upon completion of this course, you will feel confident with the Python tool ecosystem for NLP and will be able to perform state-of-art pattern extraction on any kind of text data.

Text Mining and Analytics: Hotel Reviews Sentiment Analysis

Using natural language processing (NLP) tools, an organization can analyze their review data and predict the sentiments of their customers. In this course, we'll learn how to implement NLP tools to solve a business problem end-to-end. To begin, learn about loading, exploring, and preprocessing business data. Next, explore various linguistic features and feature engineering methods for data and practice building machine learning (ML) models for sentiment prediction. Finally, examine the automation options available for building and deploying models. After completing this course, you will be able to solve NLP problems for enterprises end-to-end by leveraging a variety of concepts and tools.

Kenmerken

Docent inbegrepen
Bereidt voor op officieel examen
Engels (US)
14 uur
Natural Language Processing
180 dagen online toegang
HBO

Meer informatie

Doelgroep Softwareontwikkelaar
Voorkennis

Geen formele voorkennis vereist. Een basiskennis van het onderwerp wordt echter aangeraden.

Resultaat

Aan het einde van deze training beschik je over de kennis en kunde om de kracht van NLP te benutten voor het verkrijgen van inzichten en het oplossen van praktische uitdagingen met behulp van language data.

Positieve reacties van cursisten

Training: Leidinggeven aan de AI transformatie

Nuttige training. Het bestelproces verliep vlot, ik kon direct beginnen.

- Mike van Manen

Onbeperkt Leren Abonnement

Onbeperkt Leren aangeschaft omdat je veel waar voor je geld krijgt. Ik gebruik het nog maar kort, maar eerste indruk is goed.

- Floor van Dijk

Hoe gaat het te werk?

1

Training bestellen

Nadat je de training hebt besteld krijg je bevestiging per e-mail.

2

Toegang leerplatform

In de e-mail staat een link waarmee je toegang krijgt tot ons leerplatform.

3

Direct beginnen

Je kunt direct van start. Studeer vanaf nu waar en wanneer jij wilt.

4

Training afronden

Rond de training succesvol af en ontvang van ons een certificaat!

Veelgestelde vragen

Veelgestelde vragen

Op welke manieren kan ik betalen?

Je kunt bij ons betalen met iDEAL, PayPal, Creditcard, Bancontact en op factuur. Betaal je op factuur, dan kun je met de training starten zodra de betaling binnen is.

Hoe lang heb ik toegang tot de training?

Dit verschilt per training, maar meestal 180 dagen. Je kunt dit vinden onder het kopje ‘Kenmerken’.

Waar kan ik terecht als ik vragen heb?

Je kunt onze Learning & Development collega’s tijdens kantoortijden altijd bereiken via support@aitrainingscentrum.nl of telefonisch via 026-8402941.

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