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.
NobleProg instructor-led live Natural Language Process (NLP) training courses demonstrate through interactive discussion and hands-on 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 step-by-step through the process of evaluating and applying the right algorithms to analyze data and report on its significance.
NLP training is available in various formats, including onsite live training and remote live training. Onsite live training can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is available by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
I did like the exercises
- Office for National Statistics
Course Name: Natural Language Processing with Python
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Knowledgeable trainer
Sridhar Voorakkara - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
The trainer very easily explained difficult and advanced topics.
Leszek K PwC Polska Sp. z o. o
.Course Name: Artificial Intelligence Overview
Translated by
Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K - PwC Polska Sp. z o.o.
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course Name: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Translated by
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
| Code | Name | Venue | Duration | Course Date | PHP | Course Price [Remote / Classroom] |
|---|---|---|---|---|---|---|
| tsflw2v | Natural Language Processing with TensorFlow | Aberdeen - Berry Street | 35 hours | Mon, 2018-05-07 09:30 | £6500 / £8150 | |
| python_nltk | Natural Language Processing with Python | Aberdeen - Berry Street | 28 hours | Mon, 2018-05-07 09:30 | £5200 / £6520 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Aberdeen - Berry Street | 28 hours | Mon, 2018-05-14 09:30 | £5200 / £6520 | |
| nlg | Python for Natural Language Generation | Aberdeen - Berry Street | 21 hours | Wed, 2018-05-16 09:30 | £3900 / £4890 | |
| pythontextml | Python: Machine Learning with Text | Aberdeen - Berry Street | 21 hours | Thu, 2018-05-17 09:30 | £3900 / £4560 | |
| NPL_LBG | Natural Language Processing - AI/Robotics | Aberdeen - Berry Street | 21 hours | Tue, 2018-05-22 09:30 | £3900 / £4890 | |
| chatbotpython | Building Chatbots in Python | Aberdeen - Berry Street | 21 hours | Tue, 2018-05-22 09:30 | £3300 / £4290 | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Aberdeen - Berry Street | 28 hours | Tue, 2018-05-29 09:30 | £5200 / £6520 | |
| nlp | Natural Language Processing | Aberdeen - Berry Street | 21 hours | Tue, 2018-05-29 09:30 | £3900 / £4890 | |
| aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Aberdeen - Berry Street | 21 hours | Tue, 2018-06-12 09:30 | £3900 / £4890 | |
| nlpwithr | NLP: Natural Language Processing with R | Aberdeen - Berry Street | 21 hours | Wed, 2018-06-13 09:30 | £3900 / £4890 | |
| w2vdl4j | NLP with Deeplearning4j | Aberdeen - Berry Street | 14 hours | Wed, 2018-06-13 09:30 | £2600 / £3260 | |
| aiint | Artificial Intelligence Overview | Aberdeen - Berry Street | 7 hours | Fri, 2018-06-15 09:30 | £1300 / £1630 | |
| opennlp | OpenNLP for Text Based Machine Learning | Aberdeen - Berry Street | 14 hours | Thu, 2018-06-21 09:30 | £2600 / £3260 | |
| tsflw2v | Natural Language Processing with TensorFlow | Aberdeen - Berry Street | 35 hours | Mon, 2018-07-02 09:30 | £6500 / £8150 | |
| python_nltk | Natural Language Processing with Python | Aberdeen - Berry Street | 28 hours | Mon, 2018-07-02 09:30 | £5200 / £6520 | |
| nlg | Python for Natural Language Generation | Aberdeen - Berry Street | 21 hours | Mon, 2018-07-09 09:30 | £3900 / £4890 | |
| pythontextml | Python: Machine Learning with Text | Aberdeen - Berry Street | 21 hours | Mon, 2018-07-09 09:30 | £3900 / £4890 | |
| textsum | Text Summarization with Python | Aberdeen - Berry Street | 14 hours | Tue, 2018-07-10 09:30 | £2200 / £2860 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Aberdeen - Berry Street | 28 hours | Tue, 2018-07-10 09:30 | £5200 / £6520 | |
| NPL_LBG | Natural Language Processing - AI/Robotics | Aberdeen - Berry Street | 21 hours | Tue, 2018-07-17 09:30 | £3900 / £4890 | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Aberdeen - Berry Street | 28 hours | Tue, 2018-07-24 09:30 | £5200 / £6520 | |
| nlp | Natural Language Processing | Aberdeen - Berry Street | 21 hours | Tue, 2018-07-24 09:30 | £3900 / £4890 | |
| aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Aberdeen - Berry Street | 21 hours | Tue, 2018-08-07 09:30 | £3900 / £4890 | |
| nlpwithr | NLP: Natural Language Processing with R | Aberdeen - Berry Street | 21 hours | Mon, 2018-08-13 09:30 | £3900 / £4890 | |
| w2vdl4j | NLP with Deeplearning4j | Aberdeen - Berry Street | 14 hours | Tue, 2018-08-14 09:30 | £2600 / £3260 | |
| chatbotpython | Building Chatbots in Python | Aberdeen - Berry Street | 21 hours | Wed, 2018-08-15 09:30 | £3300 / £4290 | |
| aiint | Artificial Intelligence Overview | Aberdeen - Berry Street | 7 hours | Wed, 2018-08-15 09:30 | £1300 / £1630 | |
| opennlp | OpenNLP for Text Based Machine Learning | Aberdeen - Berry Street | 14 hours | Thu, 2018-08-16 09:30 | £2600 / £3260 | |
| tsflw2v | Natural Language Processing with TensorFlow | Aberdeen - Berry Street | 35 hours | Mon, 2018-09-03 09:30 | £6500 / £8150 | |
| pythontextml | Python: Machine Learning with Text | Aberdeen - Berry Street | 21 hours | Mon, 2018-09-03 09:30 | £3900 / £4890 | |
| python_nltk | Natural Language Processing with Python | Aberdeen - Berry Street | 28 hours | Tue, 2018-09-04 09:30 | £5200 / £6520 | |
| nlg | Python for Natural Language Generation | Aberdeen - Berry Street | 21 hours | Mon, 2018-09-10 09:30 | £3900 / £4890 | |
| textsum | Text Summarization with Python | Aberdeen - Berry Street | 14 hours | Mon, 2018-09-10 09:30 | £2200 / £2860 | |
| NPL_LBG | Natural Language Processing - AI/Robotics | Aberdeen - Berry Street | 21 hours | Tue, 2018-09-18 09:30 | £3900 / £4890 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Aberdeen - Berry Street | 28 hours | Tue, 2018-09-18 09:30 | £5200 / £6520 | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Aberdeen - Berry Street | 28 hours | Tue, 2018-09-25 09:30 | £5200 / £6520 | |
| nlp | Natural Language Processing | Aberdeen - Berry Street | 21 hours | Wed, 2018-09-26 09:30 | £3900 / £4890 | |
| aiint | Artificial Intelligence Overview | Aberdeen - Berry Street | 7 hours | Tue, 2018-10-09 09:30 | £1300 / £1630 | |
| nlpwithr | NLP: Natural Language Processing with R | Aberdeen - Berry Street | 21 hours | Mon, 2018-10-15 09:30 | £3900 / £4890 | |
| opennlp | OpenNLP for Text Based Machine Learning | Aberdeen - Berry Street | 14 hours | Wed, 2018-10-17 09:30 | £2600 / £3260 | |
| aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Aberdeen - Berry Street | 21 hours | Wed, 2018-10-17 09:30 | £3900 / £4890 | |
| w2vdl4j | NLP with Deeplearning4j | Aberdeen - Berry Street | 14 hours | Thu, 2018-10-18 09:30 | £2600 / £3260 | |
| pythontextml | Python: Machine Learning with Text | Aberdeen - Berry Street | 21 hours | Wed, 2018-10-24 09:30 | £3900 / £4890 | |
| tsflw2v | Natural Language Processing with TensorFlow | Aberdeen - Berry Street | 35 hours | Mon, 2018-10-29 09:30 | £6500 / £8150 | |
| chatbotpython | Building Chatbots in Python | Aberdeen - Berry Street | 21 hours | Mon, 2018-10-29 09:30 | £3300 / £4290 | |
| python_nltk | Natural Language Processing with Python | Aberdeen - Berry Street | 28 hours | Mon, 2018-10-29 09:30 | £5200 / £6520 | |
| textsum | Text Summarization with Python | Aberdeen - Berry Street | 14 hours | Tue, 2018-10-30 09:30 | £2200 / £2860 | |
| nlg | Python for Natural Language Generation | Aberdeen - Berry Street | 21 hours | Mon, 2018-11-05 09:30 | £3900 / £4890 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Aberdeen - Berry Street | 28 hours | Mon, 2018-11-12 09:30 | £5200 / £6520 | |
| NPL_LBG | Natural Language Processing - AI/Robotics | Aberdeen - Berry Street | 21 hours | Wed, 2018-11-14 09:30 | £3900 / £4890 |
| Code | Name | Duration | Outline |
|---|---|---|---|
| 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:
Audience
Format of the course
Note
|
| 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. |
| 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. |
| 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:
|
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | 28 hours |
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| 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 Format of the course |
| 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:
Audience
Format of the course
|
| 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:
Audience
Format of the course
|
| 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:
Audience
Format of the course
|
| 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:
Audience
Format of the course
|
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours |
Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) 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:
Audience
Format of the course
|