Deep Learning Training in Jersey, CI

Deep Learning Training in Jersey, CI

Local, instructor-led live Deep Learning (DL) training courses demonstrate through hands-on practice the fundamentals and applications of Deep Learning and cover subjects such as deep machine learning, deep structured learning, and hierarchical learning. Deep Learning training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Jersey, CI or in NobleProg corporate training centers in Jersey, CI. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

St Helier, Jersey, Channel Isles
Learn Deep Learning in our training center in Jersey, CI.

Business Hours
Monday-Friday: 8:30am – 6pm

Phone
01534 828 585
01534 828 587

Facilities:

  • The Hub is equipped with free Gigabit Wi-Fi.
  • Breakout Zone
    • This is a great open space for collaboration, a brain break or for small meetings of between nine and 12 people. It includes a SmartTV for presentations.
  • Skill Zone
    • An area...
Read more

Testimonials

★★★★★
★★★★★

Deep Learning Subcategories

Deep Learning Course Events - Jersey, CI

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
annmldtArtificial Neural Networks, Machine Learning, Deep ThinkingSt Helier, Jersey, Channel Isles21 hoursWed, 2018-10-10 09:30£3900 / £5325
mlentreMachine Learning Concepts for Entrepreneurs and ManagersSt Helier, Jersey, Channel Isles21 hoursWed, 2018-10-10 09:30£3900 / £5325
facebooknmtFacebook NMT: Setting up a Neural Machine Translation SystemSt Helier, Jersey, Channel Isles7 hoursWed, 2018-10-10 09:30£1100 / £1575
mlbankingpython_Machine Learning for Banking (with Python)St Helier, Jersey, Channel Isles21 hoursWed, 2018-10-10 09:30£3900 / £5325
opennmtOpenNMT: Setting Up a Neural Machine Translation SystemSt Helier, Jersey, Channel Isles7 hoursFri, 2018-10-12 09:30£1100 / £1575
NeuralnettfNeural Networks Fundamentals using TensorFlow as ExampleSt Helier, Jersey, Channel Isles28 hoursMon, 2018-10-15 09:30£5200 / £7100
dlforbankingwithrDeep Learning for Banking (with R)St Helier, Jersey, Channel Isles28 hoursMon, 2018-10-15 09:30£5200 / £7100
embeddingprojectorEmbedding Projector: Visualizing Your Training DataSt Helier, Jersey, Channel Isles14 hoursMon, 2018-10-15 09:30£2200 / £3150
bspkannmldtArtificial Neural Networks, Machine Learning and Deep ThinkingSt Helier, Jersey, Channel Isles21 hoursTue, 2018-10-16 09:30£3900 / £5325
radvmlAdvanced Machine Learning with RSt Helier, Jersey, Channel Isles21 hoursTue, 2018-10-16 09:30£3900 / £5325
dlforbankingwithpythonDeep Learning for Banking (with Python)St Helier, Jersey, Channel Isles28 hoursTue, 2018-10-16 09:30£5200 / £7100
PaddlePaddlePaddlePaddleSt Helier, Jersey, Channel Isles21 hoursTue, 2018-10-16 09:30N/A / £4725
dl4jirDeepLearning4J for Image RecognitionSt Helier, Jersey, Channel Isles21 hoursWed, 2018-10-17 09:30£3900 / £5325
tf101Deep Learning with TensorFlowSt Helier, Jersey, Channel Isles21 hoursWed, 2018-10-17 09:30£3900 / £5325
openfaceOpenFace: Creating Facial Recognition SystemsSt Helier, Jersey, Channel Isles14 hoursWed, 2018-10-17 09:30£2600 / £3550
tensorflowservingTensorFlow ServingSt Helier, Jersey, Channel Isles7 hoursFri, 2018-10-19 09:30£1300 / £1775
deeplrn深度学习基础与实战St Helier, Jersey, Channel Isles14 hoursMon, 2018-10-22 09:30£2200 / £3150
intrdplrngrsneuingIntroduction Deep Learning & Neural Networks for EngineersSt Helier, Jersey, Channel Isles21 hoursMon, 2018-10-22 09:30£3300 / £4725
dlfortelecomwithpythonDeep Learning for Telecom (with Python)St Helier, Jersey, Channel Isles28 hoursMon, 2018-10-22 09:30£4400 / £6300
t2tT2T: Creating Sequence to Sequence Models for Generalized LearningSt Helier, Jersey, Channel Isles7 hoursThu, 2018-10-25 09:30£1100 / £1575
dlfornlpDeep Learning for NLP (Natural Language Processing)St Helier, Jersey, Channel Isles28 hoursMon, 2018-10-29 09:30£5200 / £7100
tsflw2vNatural Language Processing with TensorFlowSt Helier, Jersey, Channel Isles35 hoursMon, 2018-10-29 09:30£6500 / £8875
undnnUnderstanding Deep Neural NetworksSt Helier, Jersey, Channel Isles35 hoursMon, 2018-10-29 09:30£6500 / £8875
dlforfinancewithpythonDeep Learning for Finance (with Python)St Helier, Jersey, Channel Isles28 hoursMon, 2018-10-29 09:30£5200 / £7100
caffeDeep Learning for Vision with CaffeSt Helier, Jersey, Channel Isles21 hoursTue, 2018-10-30 09:30£3900 / £5325
dlfinancewithrDeep Learning for Finance (with R)St Helier, Jersey, Channel Isles28 hoursTue, 2018-10-30 09:30£5200 / £7100
pythonadvmlPython for Advanced Machine LearningSt Helier, Jersey, Channel Isles21 hoursWed, 2018-10-31 09:30£3900 / £5325
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing UnitsSt Helier, Jersey, Channel Isles7 hoursThu, 2018-11-01 09:30£1300 / £1775
MicrosoftCognitiveToolkitMicrosoft Cognitive Toolkit 2.xSt Helier, Jersey, Channel Isles21 hoursMon, 2018-11-05 09:30N/A / £4725
dlvDeep Learning for VisionSt Helier, Jersey, Channel Isles21 hoursTue, 2018-11-06 09:30£3900 / £5325

Deep Learning Course Outlines in Jersey, CI

CodeNameDurationOverview
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.
mldlnlpintroML、DL與NLP入門與進階大綱14 hoursThe aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
dlforbankingwithrDeep Learning for Banking (with R)28 hoursMachine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.

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

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use R to create deep learning models for banking
- Build their own deep learning credit risk model using R

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dlforbankingwithpythonDeep Learning for Banking (with Python)28 hoursMachine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

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

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dlfinancewithrDeep Learning for Finance (with R)28 hoursMachine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

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

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use R to create deep learning models for finance
- Build their own deep learning stock price prediction model using R

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
undnnUnderstanding Deep Neural Networks35 hoursThis course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

-

have a good understanding on deep neural networks(DNN), CNN and RNN

-

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, building graphs and logging

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.
mlbankingpython_Machine Learning for Banking (with Python)21 hoursMachine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
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
radvmlAdvanced Machine Learning with R21 hoursIn this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

- Use techniques as hyper-parameter tuning and deep learning
- Understand and implement unsupervised learning techniques
- Put a model into production for use in a larger application

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
pythonadvmlPython for Advanced Machine Learning21 hoursIn this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

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

- Implement machine learning algorithms and techniques for solving complex problems
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data
- Push Python algorithms to their maximum potential
- Use libraries and packages such as NumPy and Theano

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
openfaceOpenFace: Creating Facial Recognition Systems14 hoursOpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.

In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.

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

- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
t2tT2T: Creating Sequence to Sequence Models for Generalized Learning7 hoursTensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.

In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.

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

- Install tensor2tensor, select a data set, and train and evaluate an AI model
- Customize a development environment using the tools and components included in Tensor2Tensor
- Create and use a single model to concurrently learn a number of tasks from multiple domains
- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
- Obtain satisfactory processing results using a single GPU

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dsstneAmazon DSSTNE: Build a Recommendation System7 hoursAmazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host.

In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.

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

- Train a recommendation model with sparse datasets as input
- Scale training and prediction models over multiple GPUs
- Spread out computation and storage in a model-parallel fashion
- Generate Amazon-like personalized product recommendations
- Deploy a production-ready application that can scale at heavy workloads

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
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
deeplearning1Introduction to Deep Learning21 hoursThis course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
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.
facebooknmtFacebook NMT: Setting up a Neural Machine Translation System7 hoursFacebook NMT (Fairseq) is an open-source sequence-to-sequence learning toolkit created by Facebook for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

- Localization specialists with a technical background
- Global content managers
- Localization engineers
- Software developers in charge of implementing global content solutions

Format of the course

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

Note

- If you wish to use specific source and target language content, please contact us to arrange.
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.
opennmtOpenNMT: Setting Up a Neural Machine Translation System7 hoursOpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit.

In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.

By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.

Source and target language samples will be pre-arranged per the audience's requirements.

Audience

- Localization specialists with a technical background
- Global content managers
- Localization engineers
- Software developers in charge of implementing global content solutions

Format of the course

- Part lecture, part discussion, heavy hands-on practice
mlentreMachine Learning Concepts for Entrepreneurs and Managers21 hoursThis training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same.

Target Audience

- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
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.
TorchTorch: Getting started with Machine and Deep Learning21 hoursTorch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others.

In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.

By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.

Audience
Software developers and programmers wishing to enable Machine and Deep Learning within their applications

Format of the course
Overview of Machine and Deep Learning
In-class coding and integration exercises
Test questions sprinkled along the way to check understanding
mldtMachine Learning and Deep Learning21 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning)
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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
dlvDeep Learning for Vision21 hoursAudience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images

This course provide working examples.
caffeDeep Learning for Vision with Caffe21 hoursCaffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

After completing this course, delegates will be able to:

- understand Caffe’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, implementing layers and logging
dladvAdvanced Deep Learning28 hoursMachine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.
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.
dlforfinancewithpythonDeep Learning for Finance (with Python)28 hoursMachine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

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

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
Deep Learning training courses in Jersey, CI, Weekend Deep Learning courses in Jersey, CI, Evening Deep Learning training in Jersey, CI, Deep Learning instructor-led in Jersey, CI, Deep Learning trainer in Jersey, CI, Deep Learning instructor-led in Jersey, CI, Deep Learning on-site in Jersey, CI, Deep Learning private courses in Jersey, CI, Weekend Deep Learning training in Jersey, CI, Evening Deep Learning courses in Jersey, CI, Deep Learning boot camp in Jersey, CI, Deep Learning one on one training in Jersey, CI, Deep Learning coaching in Jersey, CI, Deep Learning classes in Jersey, CI, Deep Learning instructor in Jersey, CI

Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Introduction to Ansible Automation London, Hatton Garden Mon, 2018-10-08 09:30 £1089 / £1464
Jenkins: Continuous Integration for Agile Development Manchester, King Street Thu, 2018-10-18 09:30 £2574 / £3224
Introduction to Recommendation Systems Swansea- Princess House Thu, 2018-10-18 09:30 £990 / £1140
Advanced Statistics using SPSS Predictive Analytics Software Birmingham Mon, 2018-10-22 09:30 £5148 / £6448
Impact Evaluation – Quantitative Analysis London, Hatton Garden Wed, 2018-10-24 09:30 £2574 / £3324
CakePHP: Rapid Web Application Development Birmingham Tue, 2018-11-06 09:30 £4356 / £5656

Course Discounts Newsletter

We respect the privacy of your email address. We will not pass on or sell your address to others.
You can always change your preferences or unsubscribe completely.

Some of our clients

is growing fast!

We are looking to expand our presence in your region!

As a Business Development Manager you will:

  • expand business in the region
  • recruit local talent (sales, agents, trainers, consultants)
  • recruit local trainers and consultants

We offer:

  • Artificial Intelligence and Big Data systems to support your local operation
  • high-tech automation
  • continuously upgraded course catalogue and content
  • good fun in international team

If you are interested in running a high-tech, high-quality training and consulting business.

contact us right away!