Artificial Intelligence (AI) Training in Exeter

Artificial Intelligence (AI) Training in Exeter

Local, instructor-led live Artificial Intelligence (AI) training courses demonstrate through hands-on practice how to implement AI solutions for solving real-world problems. AI training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Exeter or in NobleProg corporate training centres in Exeter. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider.

Exeter - The Senate
Learn Artificial Intelligence (AI) in our training center in Exeter. The business centre is located in Exeter, with excellent transport links that is served by two mainline train stations, Exeter Central and Exeter St Davids, which provide regular rail services to all major UK cities. Exeter has been identified by as one of the top 10 most profitable business locations and the county of Devon is regularly voted as number one in England for quality of life. The Senate is the finest office building in Exeter and offers the largest available floors. This... Read more

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Artificial Intelligence (AI) Course Events - Exeter

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
radvmlAdvanced Machine Learning with RExeter - The Senate21 hoursWed, 2018-11-28 09:30£3900 / £4800
pythoncomputervisionComputer Vision with PythonExeter - The Senate14 hoursWed, 2018-11-28 09:30£2600 / £3200
chatbotpythonBuilding Chatbots in PythonExeter - The Senate21 hoursWed, 2018-11-28 09:30£3300 / £4200
digitalinsurancemanagersInsurtech: A Practical Introduction for ManagersExeter - The Senate14 hoursWed, 2018-12-05 09:30£2200 / £2800
matlabpredanalyticsMatlab for Predictive AnalyticsExeter - The Senate21 hoursWed, 2018-12-05 09:30£3900 / £4800
mlentreMachine Learning Concepts for Entrepreneurs and ManagersExeter - The Senate21 hoursTue, 2018-12-04 09:30£3900 / £4800
quantumcomputingqsharpGetting Started with Quantum Computing and Q#Exeter - The Senate14 hoursThu, 2018-11-29 09:30£2600 / £3200
opennmtOpenNMT: Setting Up a Neural Machine Translation SystemExeter - The Senate7 hoursWed, 2018-12-05 09:30£1100 / £1400
dmmlrData Mining & Machine Learning with RExeter - The Senate14 hoursThu, 2018-12-06 09:30£2600 / £3200
t2tT2T: Creating Sequence to Sequence Models for Generalized LearningExeter - The Senate7 hoursThu, 2018-12-06 09:30£1100 / £1400
fijiFiji: Introduction to Scientific Image ProcessingExeter - The Senate21 hoursWed, 2018-12-05 09:30£3300 / £4200
mlfsasMachine Learning Fundamentals with Scala and Apache SparkExeter - The Senate14 hoursMon, 2018-12-03 09:30£2600 / £3200
tf101Deep Learning with TensorFlowExeter - The Senate21 hoursTue, 2018-12-04 09:30£3900 / £4800
mldtMachine Learning and Deep LearningExeter - The Senate21 hoursWed, 2018-12-05 09:30£3900 / £4800
dl4jirDeepLearning4J for Image RecognitionExeter - The Senate21 hoursWed, 2018-12-05 09:30£3900 / £4800
nlpNatural Language ProcessingExeter - The Senate21 hoursWed, 2018-12-05 09:30£3900 / £4800
ml_lbgMachine Learning – Data scienceExeter - The Senate21 hoursMon, 2018-12-03 09:30£3900 / £4800
dlforfinancewithpythonDeep Learning for Finance (with Python)Exeter - The Senate28 hoursTue, 2018-12-04 09:30£5200 / £6400
patternmatchingPattern MatchingExeter - The Senate14 hoursThu, 2018-12-06 09:30£2600 / £3200
microsoftcognitivetoolkitMicrosoft Cognitive Toolkit 2.xExeter - The Senate21 hoursTue, 2018-12-04 09:30N/A / £4200
matlabml1Introduction to Machine Learning with MATLABExeter - The Senate21 hoursWed, 2018-12-12 09:30£3300 / £4200
mllbgMachine Learning in business – AI/RoboticsExeter - The Senate14 hoursThu, 2018-12-13 09:30£2600 / £3200
dlvDeep Learning for VisionExeter - The Senate21 hoursTue, 2018-12-11 09:30£3900 / £4800
bigdatabicriminalBig Data Business Intelligence for Criminal Intelligence AnalysisExeter - The Senate35 hoursMon, 2018-12-10 09:30£6500 / £8000
aiintArtificial Intelligence OverviewExeter - The Senate7 hoursThu, 2018-12-13 09:30£1300 / £1600
mlfwr1Machine Learning Fundamentals with RExeter - The Senate14 hoursTue, 2018-12-11 09:30£2600 / £3200
evolvoEvolving Objects (EO) Exeter - The Senate21 hoursMon, 2018-12-10 09:30£3300 / £4200
intelligentmobileappsBuilding Intelligent Mobile ApplicationsExeter - The Senate35 hoursMon, 2018-12-10 09:30£5500 / £7000
mlfinancerMachine Learning for Finance (with R)Exeter - The Senate28 hoursMon, 2018-12-10 09:30£5200 / £6400
aisoc_lbgAI in business and Society & The future of AI - AI/RoboticsExeter - The Senate7 hoursTue, 2018-12-11 09:30£1300 / £1600

Artificial Intelligence (AI) Course Outlines in Exeter

CodeNameDurationOverview
aiautoArtificial Intelligence in Automotive14 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
intrdplrngrsneuingIntroduction Deep Learning & Neural Networks for Engineers21 hoursArtificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
optapracOptaPlanner in Practice21 hoursThis course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
OpenNNOpenNN: Implementing Neural Networks14 hoursOpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning.

In this course we go over the principles of neural networks and use OpenNN to implement a sample application.

Audience
Software developers and programmers wishing to create Deep Learning applications.

Format of the course
Lecture and discussion coupled with hands-on exercises.
opennlpOpenNLP for Text Based Machine Learning14 hoursThe 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
Nue_LBGNeural computing – Data science14 hoursThis classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
NPL_LBGNatural Language Processing - AI/Robotics21 hoursThis 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
nlpwithrNLP: Natural Language Processing with R21 hoursIt 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
nlpNatural Language Processing21 hoursThis 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.
nlgPython for Natural Language Generation21 hoursNatural 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
NeuralnettfNeural Networks Fundamentals using TensorFlow as Example28 hoursThis 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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
neuralnetIntroduction to the use of neural networks7 hoursThe training is aimed at people who want to learn the basics of neural networks and their applications.
MicrosoftCognitiveToolkitMicrosoft Cognitive Toolkit 2.x21 hoursMicrosoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.

By the end of this training, participants will be able to:

- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
matlabdlMatlab for Deep Learning14 hoursIn this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters

Audience

- Developers
- Engineers
- Domain experts

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
genealgoGenetic Algorithms28 hoursThis four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
aiintArtificial Intelligence Overview7 hoursThis 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.
encogintroEncog: Introduction to Machine Learning14 hoursEncog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

By the end of this training, participants will be able to:

- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
encogadvEncog: Advanced Machine Learning14 hoursEncog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.

By the end of this training, participants will be able to:

- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
drlpythonDeep Reinforcement Learning with Python21 hoursDeep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

By the end of this training, participants will be able to:

- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent

Audience

- Developers
- Data Scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dlfornlpDeep Learning for NLP (Natural Language Processing)28 hoursDeep 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
datamodelingPattern Recognition35 hoursThis course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.

Audience
Data analysts
PhD students, researchers and practitioners
bspkannmldtArtificial Neural Networks, Machine Learning and Deep Thinking21 hoursArtificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
appliedmlApplied Machine Learning14 hoursThis training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.
appaipyApplied AI from Scratch in Python28 hoursThis is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
appaiApplied AI from Scratch28 hoursThis is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
annmldtArtificial Neural Networks, Machine Learning, Deep Thinking21 hoursArtificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
aitechArtificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP21 hoursThis 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.
AISoc_LBGAI in business and Society & The future of AI - AI/Robotics7 hoursThis is a classroom based training session in a presentation and Q&A format
aiintrozeroFrom Zero to AI35 hoursThis course is created for people who have no previous experience in probability and statistics.
PaddlePaddlePaddlePaddle21 hoursPaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.

In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.

By the end of this training, participants will be able to:

- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Android Development Glasgow Mon, 2018-11-19 09:30 £4356 / £5756
Selenium WebDriver in C#: Introduction to Web Testing Automation in C# Edinburgh Training and Conference Venue Tue, 2018-11-20 09:30 £2178 / £2578
F#: Introduction to Functional Programming Portsmouth Mon, 2018-11-26 09:30 £2178 / £2478
Python: Automate the Boring Stuff Swindon Wed, 2018-11-28 09:30 £2178 / £2528
JMeter Fundamentals and JMeter Advanced Edinburgh Training and Conference Venue Mon, 2018-12-03 09:30 £2178 / £2578
HAProxy Administration London, Hatton Garden Mon, 2018-12-03 09:30 £2178 / £2928
From Data to Decision with Big Data and Predictive Analytics Birmingham Tue, 2018-12-11 09:30 £3861 / £4836
Social Media Marketing Oxford Wed, 2018-12-12 09:30 N/A / £1364
Test Automation with Selenium Manchester, King Street Wed, 2018-12-12 09:30 £3267 / £4242
Artificial Intelligence Overview London, Hatton Garden Thu, 2018-12-27 09:30 £1287 / £1662
RPA: Implementing Robotic Process Automation London, Hatton Garden Tue, 2019-01-29 09:30 £3861 / £4986

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