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  1. Artificial Intelligence Training
  2. NLP Training Courses

NLP Training Courses

NLP Training

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

Client Testimonials

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

I did like the exercises

- Office for National Statistics

Course Name: Natural Language Processing with Python

The trainer very easily explained difficult and advanced topics.

Leszek K PwC Polska Sp. z o. o

.

Course Name: Artificial Intelligence Overview

Translated by Google Translated

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 Google Translated

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

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NLP Course Outlines

Code Name Duration Overview
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: 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 Audience Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing)  Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
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.
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.
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: 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
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.
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
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.
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

Upcoming Courses

CourseCourse DateCourse Price [Remote / Classroom]
Neural Networks Fundamentals using TensorFlow as Example - Winnipeg - 201 Portage AvenueMon, May 7 2018, 9:30 amCA$9650 / CA$11790
Natural Language Processing with Python - NS, Halifax - Hampton InnMon, May 7 2018, 9:30 amCA$9650 / CA$12150
Python: Machine Learning with Text - Burnaby - MetrotownTue, May 8 2018, 9:30 amCA$6350 / CA$8180
Text Summarization with Python - Calgary - Sun LifeTue, May 8 2018, 9:30 amCA$4300 / CA$6160
NLP: Natural Language Processing with R - Mississauga - AirwaysWed, May 16 2018, 9:30 amCA$6350 / CA$8240

Other regions

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Other countries

These courses are also available in other countries ››

Consulting

NLP Consulting
Weekend NLP courses, Evening NLP training, NLP boot camp, NLP instructor-led , NLP instructor, NLP trainer , NLP coaching, NLP on-site, NLP classes,Weekend NLP training, NLP training courses, Evening NLP courses, NLP one on one training

Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Hadoop for Developers (2 days) London - London City Centre Thu, May 10 2018, 9:30 am CA$4545 / CA$6365
Introduction to Recommendation Systems Burnaby Centre Fri, May 11 2018, 9:30 am CA$2475 / CA$4110
MySQL Administration Victoria - The Atrium Tue, May 29 2018, 9:30 am CA$10346 / CA$12196
Lua Embedding Toronto - West Toronto - Etobicoke Thu, Jun 7 2018, 9:30 am CA$3870 / CA$5630
Business Rule Management (BRMS) with Drools Calgary - Macleod Place II Thu, Jul 12 2018, 9:30 am CA$2475 / CA$4155

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Outline Tag Cloud

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