Deep Learning Training in Cardiff

Deep Learning Training in Cardiff

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 Cardiff or in NobleProg corporate training centres in Cardiff. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

Cardiff, Cardiff Bay
Learn Deep Learning in our training center in Cardiff.

The training rooms are located under a 8 minute walk from Cardiff Bay Rail station.

 

Enjoy your training with NobleProg in Cardiff, Wales' Capital city where, during your leisure time you'll find a host of unique attractions, superb shopping and if you decide to stay there is also top class entertainment - all within walking distance.

 

Wander through the centre where you will see historic buildings and then to Cardiff Bay which offers a...

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Deep Learning Subcategories

Deep Learning Course Events - Cardiff

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
torchTorch: Getting started with Machine and Deep LearningCardiff21 hoursWed, 2018-11-28 09:30N/A / £4800
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing UnitsCardiff7 hoursMon, 2018-12-03 09:30£1300 / £1600
dl4jirDeepLearning4J for Image RecognitionCardiff21 hoursWed, 2018-12-05 09:30£3900 / £4800
opennnOpenNN: Implementing Neural NetworksCardiff14 hoursMon, 2018-12-10 09:30£2600 / £3200
radvmlAdvanced Machine Learning with RCardiff21 hoursWed, 2018-12-05 09:30£3900 / £4800
dlvDeep Learning for VisionCardiff21 hoursMon, 2018-12-03 09:30£3900 / £4800
dladvAdvanced Deep LearningCardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
tfirTensorFlow for Image RecognitionCardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
embeddingprojectorEmbedding Projector: Visualizing Your Training DataCardiff14 hoursThu, 2018-12-13 09:30£2200 / £2800
dlfinancewithrDeep Learning for Finance (with R)Cardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
t2tT2T: Creating Sequence to Sequence Models for Generalized LearningCardiff7 hoursThu, 2018-12-13 09:30£1100 / £1400
pythonadvmlPython for Advanced Machine LearningCardiff21 hoursTue, 2018-12-18 09:30£3900 / £4800
tf101Deep Learning with TensorFlowCardiff21 hoursTue, 2018-12-18 09:30£3900 / £4800
w2vdl4jNLP with Deeplearning4jCardiff14 hoursTue, 2018-12-18 09:30£2600 / £3200
matlabdlMatlab for Deep LearningCardiff14 hoursTue, 2018-12-18 09:30£2600 / £3200
annmldtArtificial Neural Networks, Machine Learning, Deep ThinkingCardiff21 hoursWed, 2018-12-19 09:30£3900 / £4800
mldtMachine Learning and Deep LearningCardiff21 hoursWed, 2018-12-05 09:30£3900 / £4800
dlformedicineDeep Learning for MedicineCardiff14 hoursThu, 2018-12-27 09:30£2600 / £3200
bspkannmldtArtificial Neural Networks, Machine Learning and Deep ThinkingCardiff21 hoursWed, 2018-12-26 09:30£3900 / £4800
openfaceOpenFace: Creating Facial Recognition SystemsCardiff14 hoursThu, 2018-12-27 09:30£2600 / £3200
drlpythonDeep Reinforcement Learning with PythonCardiff21 hoursTue, 2019-01-01 09:30£3900 / £4800
dlforbankingwithpythonDeep Learning for Banking (with Python)Cardiff28 hoursMon, 2018-12-31 09:30£5200 / £6400
dlfornlpDeep Learning for NLP (Natural Language Processing)Cardiff28 hoursTue, 2019-01-01 09:30£5200 / £6400
dlforfinancewithpythonDeep Learning for Finance (with Python)Cardiff28 hoursTue, 2019-01-01 09:30£5200 / £6400
opennmtOpenNMT: Setting Up a Neural Machine Translation SystemCardiff7 hoursThu, 2019-01-03 09:30£1100 / £1400
undnnUnderstanding Deep Neural NetworksCardiff35 hoursMon, 2018-12-31 09:30£6500 / £8000
tensorflowservingTensorFlow ServingCardiff7 hoursFri, 2018-12-28 09:30£1300 / £1600
caffeDeep Learning for Vision with CaffeCardiff21 hoursWed, 2019-01-02 09:30£3900 / £4800
nue_lbgNeural computing – Data scienceCardiff14 hoursThu, 2019-01-03 09:30£2600 / £3200
facebooknmtFacebook NMT: Setting up a Neural Machine Translation SystemCardiff7 hoursMon, 2019-01-07 09:30£1100 / £1400

Deep Learning Course Outlines in Cardiff

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.
embeddingprojectorEmbedding Projector: Visualizing Your Training Data14 hoursEmbedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

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

- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
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.
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
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
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
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.
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
mldtMachine Learning and Deep Learning21 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning)
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
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
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.
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.
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
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.
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
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.
dlfortelecomwithpythonDeep Learning for Telecom (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 telecom 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 telecom
- Use Python, Keras, and TensorFlow to create deep learning models for telecom
- Build their own deep learning customer churn prediction model using Python

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
dlformedicineDeep Learning for Medicine14 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 attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.

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

- Understand the fundamentals of Deep Learning
- Learn Deep Learning techniques and their applications in the industry
- Examine issues in medicine which can be solved by Deep Learning technologies
- Explore Deep Learning case studies in medicine
- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

- Managers
- Medical professionals in leadership roles

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.
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
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
DLAITEDMDeep Learning AI Techniques for Executives, Developers and Managers21 hoursIntroduction:

Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.

This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.

Target Audience:

( Depending on target audience, course materials will be customized)

Executives

A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.

Project Managers

How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.

Developers

In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.

Salespersons

A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.
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.
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.
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
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

CourseVenueCourse DateCourse Price [Remote / Classroom]
Android DevelopmentGlasgowMon, 2018-11-19 09:30£4356 / £5756
Selenium WebDriver in C#: Introduction to Web Testing Automation in C#Edinburgh Training and Conference VenueTue, 2018-11-20 09:30£2178 / £2578
F#: Introduction to Functional ProgrammingPortsmouthMon, 2018-11-26 09:30£2178 / £2478
Python: Automate the Boring StuffSwindonWed, 2018-11-28 09:30£2178 / £2528
JMeter Fundamentals and JMeter AdvancedEdinburgh Training and Conference VenueMon, 2018-12-03 09:30£2178 / £2578
HAProxy AdministrationLondon, Hatton GardenMon, 2018-12-03 09:30£2178 / £2928
From Data to Decision with Big Data and Predictive AnalyticsBirmingham Tue, 2018-12-11 09:30£3861 / £4836
Test Automation with SeleniumManchester, King StreetWed, 2018-12-12 09:30£3267 / £4242
Artificial Intelligence OverviewLondon, Hatton GardenThu, 2018-12-27 09:30£1287 / £1662
RPA: Implementing Robotic Process AutomationLondon, Hatton GardenTue, 2019-01-29 09:30£3861 / £4986

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