Το τοπικό εκπαιδευτικό πρόγραμμα φυσικής γλώσσας που διεξάγεται από τους εκπαιδευτές μέσω φυσικών γλωσσών (NLP) καταδεικνύει μέσω της διαδραστικής συζήτησης και της πρακτικής άσκησης πώς να εξάγετε ιδέες και νόημα από αυτά τα δεδομένα. Χρησιμοποιώντας διαφορετικές γλώσσες προγραμματισμού όπως οι βιβλιοθήκες Python και R και Natural Language Processing (NLP), οι εκπαιδεύσεις μας συνδυάζουν έννοιες και τεχνικές από την επιστήμη των υπολογιστών, την τεχνητή νοημοσύνη και την υπολογιστική γλωσσολογία, για να βοηθήσουν τους συμμετέχοντες να κατανοήσουν το νόημα των δεδομένων κειμένου. Οι προπονήσεις NLP περπατούν τους συμμετέχοντες βήμα-βήμα μέσα από τη διαδικασία αξιολόγησης και εφαρμογής των σωστών αλγορίθμων για την ανάλυση δεδομένων και την αναφορά της σημασίας τους. Η εκπαίδευση NLP είναι διαθέσιμη ως "live on-the-job training" ή "remote live training". Η επιτόπια κατάρτιση σε πραγματικό χρόνο μπορεί να πραγματοποιηθεί σε τοπικό επίπεδο στις εγκαταστάσεις του πελάτη Ελλάδα ή σε εταιρικά κέντρα κατάρτισης NobleProg στο Ελλάδα . Η απομακρυσμένη ζωντανή προπόνηση πραγματοποιείται μέσω μιας διαδραστικής, απομακρυσμένης επιφάνειας εργασίας. NobleProg - Ο τοπικός παροχέας εκπαίδευσης
Μου άρεσαν οι ασκήσεις
Office for National Statistics
Course: Natural Language Processing with Python
Translated by
Ο εκπαιδευτής πολύ εύκολα εξηγούσε τα δύσκολα και προηγμένα θέματα.
Leszek K
Course: Artificial Intelligence Overview
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Αυτό είναι ένα από τα καλύτερα hands-on με ασκήσεις μαθήματα προγραμματισμού που έχω πάρει ποτέ.
Laura Kahn
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Αυτή είναι μια από τις καλύτερες online εκπαιδευτικές ασκήσεις που έχω πάρει ποτέ στην καριέρα μου για 13 χρόνια. Συνεχίστε την καλή δουλειά!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Translated by
Αυτή είναι μια από τις καλύτερες online εκπαιδευτικές ασκήσεις που έχω πάρει ποτέ στην καριέρα μου για 13 χρόνια. Συνεχίστε την καλή δουλειά!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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| Code | Name | Duration | Overview |
|---|---|---|---|
| aiint | Artificial Intelligence Overview | 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. |
| nlp | Natural Language Processing | 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. |
| python_nltk | Natural Language Processing with Python | 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. |
| tsflw2v | Natural Language Processing with TensorFlow | 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 |
| w2vdl4j | NLP with Deeplearning4j | 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. |
| aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | 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. |
| nlpwithr | NLP: Natural Language Processing with R | 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 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. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| pythontextml | Python: Machine Learning with Text | 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 |
| nlg | Python for Natural Language Generation | 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 |
| python_nlp | Natural Language Processing with Deep Dive in Python and NLTK | 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. |
| opennlp | OpenNLP for Text Based Machine Learning | 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 |
| textsum | Text Summarization with Python | 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 |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours | 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 |
| NPL_LBG | Natural Language Processing - AI/Robotics | 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 |
| chatbotpython | Building Chatbots in 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 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. |
| spacy | Natural Language Processing (NLP) with Python spaCy | 14 hours | αυτό εκπαιδευτής-LED, Live Training (επιτόπου ή απομακρυσμένες) έχει ως στόχο την ανάπτυξη και τους επιστήμονες δεδομένων που επιθυμούν να χρησιμοποιήσουν ευρύχωρα για την επεξεργασία πολύ μεγάλες ποσότητες κειμένου για να βρείτε μοτίβα και να αποκτήσουν γνώσεις. μέχρι το τέλος αυτής της εκπαίδευσης, οι συμμετέχοντες θα είναι σε θέση να: - εγκατάσταση και ρύθμιση παραμέτρων ευρύχωρα. - κατανοήσουν ευρύχωρα & #39; s προσέγγιση για την επεξεργασία φυσικής γλώσσας (NLP). - εξαγάγετε μοτίβα και αποκτήστε επιχειρηματικές γνώσεις από μεγάλες πηγές δεδομένων. το - ενσωματώνει τη βιβλιοθήκη ευρύχωρα με τις υπάρχουσες εφαρμογές ιστού και παλαιού τύπου. - ανάπτυξη ευρύχωρα σε ζωντανά περιβάλλοντα παραγωγής για την πρόβλεψη της ανθρώπινης συμπεριφοράς. - χρήση ευρύχωρα για την προ-επεξεργασία κειμένου για τη βαθιά μάθηση μορφή του μαθήματος - διαΔραστική διάλεξη και συζήτηση. - πολλές ασκήσεις και εξάσκηση. - hands-on εφαρμογή σε ένα περιβάλλον Live-Lab. επιλογές προσαρμογής μαθημάτων - να ζητήσετε μια προσαρμοσμένη εκπαίδευση για αυτό το μάθημα, παρακαλούμε επικοινωνήστε μαζί μας για να κανονίσουμε. - για να μάθετε περισσότερα για ευρύχωρα, παρακαλούμε επισκεφθείτε την: https://spacy.io/ |
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Building Chatbots in Python - Αθήνα (Athens), Acropolis | Tue, 2019-03-05 09:30 | 4500EUR / 5300EUR |
| Building Chatbots in Python - Πάτρα (Patras) | Wed, 2019-04-03 09:30 | 4500EUR / 5300EUR |
| Building Chatbots in Python - Nicosia, Jacovides Tower | Wed, 2019-04-10 09:30 | 4500EUR / 5300EUR |
| Building Chatbots in Python - Θεσσαλονίκη (Thessaloniki) | Mon, 2019-04-15 09:30 | 4500EUR / 5300EUR |
| Building Chatbots in Python - Αθήνα (Athens), Acropolis | Tue, 2019-04-30 09:30 | 4500EUR / 5300EUR |
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Deep Learning for Finance (with R) | Αθήνα (Athens), Acropolis | Mon, 2019-02-18 09:30 | 6300EUR / 7300EUR |
| Introduction to Embedded Computers | Αθήνα (Athens), Acropolis | Mon, 2019-02-25 09:30 | 3150EUR / 3750EUR |
| Neural computing – Data science | Αθήνα (Athens), Acropolis | Mon, 2019-06-03 09:30 | 3150EUR / 3750EUR |
| Introduction to Ansible Automation | Θεσσαλονίκη (Thessaloniki) | Mon, 2019-06-10 09:30 | 1350EUR / 1750EUR |
| Introduction to R with Time Series Analysis | Αθήνα (Athens), Acropolis | Wed, 2019-07-31 09:30 | 4725EUR / 5525EUR |
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