Machine Learning Training in Cardiff

Machine Learning Training in Cardiff

Local, instructor-led live Machine Learning training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning. Machine 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 centers in Cardiff. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

Cardiff, Cardiff Bay
Learn Machine 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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Machine Learning Course Events - Cardiff

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
matlabdlMatlab for Deep LearningCardiff14 hoursWed, 2018-09-05 09:30£2600 / £3200
encogintroEncog: Introduction to Machine LearningCardiff14 hoursWed, 2018-09-05 09:30£2600 / £3200
cortanaTurning Data into Intelligent Action with Cortana IntelligenceCardiff28 hoursMon, 2018-09-10 09:30£4400 / £5600
bspkannmldtArtificial Neural Networks, Machine Learning and Deep ThinkingCardiff21 hoursMon, 2018-09-10 09:30£3900 / £4800
drlpythonDeep Reinforcement Learning with PythonCardiff21 hoursMon, 2018-09-10 09:30£3900 / £4800
dlforbankingwithrDeep Learning for Banking (with R)Cardiff28 hoursTue, 2018-09-11 09:30£5200 / £6400
dlfornlpDeep Learning for NLP (Natural Language Processing)Cardiff28 hoursTue, 2018-09-11 09:30£5200 / £6400
openfaceOpenFace: Creating Facial Recognition SystemsCardiff14 hoursWed, 2018-09-12 09:30£2600 / £3200
dmmlrData Mining & Machine Learning with RCardiff14 hoursWed, 2018-09-12 09:30£2600 / £3200
caffeDeep Learning for Vision with CaffeCardiff21 hoursWed, 2018-09-12 09:30£3900 / £4800
aifortelecomAI Awareness for TelecomCardiff14 hoursWed, 2018-09-12 09:30£2600 / £3200
dlformedicineDeep Learning for MedicineCardiff14 hoursWed, 2018-09-12 09:30£2600 / £3200
mlfunpythonMachine Learning Fundamentals with PythonCardiff14 hoursThu, 2018-09-13 09:30£2600 / £3200
aicityplanningArtificial Intelligence for City PlanningCardiff14 hoursThu, 2018-09-13 09:30£2200 / £2800
tensorflowservingTensorFlow ServingCardiff7 hoursWed, 2018-09-19 09:30£1300 / £1600
ML_LBGMachine Learning – Data scienceCardiff21 hoursWed, 2018-09-19 09:30£3900 / £4800
MicrosoftCognitiveToolkitMicrosoft Cognitive Toolkit 2.xCardiff21 hoursWed, 2018-09-19 09:30N/A / £4200
aiintrozeroFrom Zero to AICardiff35 hoursMon, 2018-09-24 09:30£6500 / £8000
datamodelingPattern RecognitionCardiff35 hoursMon, 2018-09-24 09:30£6500 / £8000
NeuralnettfNeural Networks Fundamentals using TensorFlow as ExampleCardiff28 hoursMon, 2018-09-24 09:30£5200 / £6400
appliedmlApplied Machine LearningCardiff14 hoursMon, 2018-09-24 09:30£2600 / £3200
spmllibApache Spark MLlibCardiff35 hoursMon, 2018-09-24 09:30£6500 / £8000
mlbankingpython_Machine Learning for Banking (with Python)Cardiff21 hoursMon, 2018-09-24 09:30£3900 / £4800
systemmlApache SystemML for Machine LearningCardiff14 hoursMon, 2018-09-24 09:30£2600 / £3200
tsflw2vNatural Language Processing with TensorFlowCardiff35 hoursMon, 2018-09-24 09:30£6500 / £8000
deeplearning1Introduction to Deep LearningCardiff21 hoursTue, 2018-09-25 09:30£3900 / £4800
mlentreMachine Learning Concepts for Entrepreneurs and ManagersCardiff21 hoursTue, 2018-09-25 09:30£3900 / £4800
facebooknmtFacebook NMT: Setting up a Neural Machine Translation SystemCardiff7 hoursTue, 2018-09-25 09:30£1100 / £1400
textsumText Summarization with PythonCardiff14 hoursTue, 2018-09-25 09:30£2200 / £2800
matlabml1Introduction to Machine Learning with MATLABCardiff21 hoursTue, 2018-09-25 09:30£3300 / £4200

Machine Learning Course Outlines in Cardiff

CodeNameDurationOverview
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.
mldtMachine Learning and Deep Learning21 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning)
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
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
snorkelSnorkel: Rapidly Process Training Data7 hoursSnorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.

In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.

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

- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
facebooknmtFacebook NMT: Setting up a Neural Machine Translation System7 hoursFairseq is an open-source sequence-to-sequence learning toolkit created by Facebok 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.
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
octnpOctave not only for programmers21 hoursCourse is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
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
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
wolfdataData Science: Analysis and Presentation7 hoursThe Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.
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.
mlintroIntroduction to Machine Learning7 hoursThis training course is for people that would like to apply basic Machine Learning techniques in practical applications.

Audience

Data scientists and statisticians that have some familiarity with machine learning and know how to program R. 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 a practical introduction 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.
aiintrozeroFrom Zero to AI35 hoursThis course is created for people who have no previous experience in probability and statistics.
systemmlApache SystemML for Machine Learning14 hoursApache SystemML is a distributed and declarative machine learning platform.

SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.

Audience

This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.
predioMachine Learning with PredictionIO21 hoursPredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.

Audience

This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
dmmlrData Mining & Machine Learning with R14 hoursR is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
mlfsasMachine Learning Fundamentals with Scala and Apache Spark14 hoursThe aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala 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.
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.
matlabml1Introduction to Machine Learning with MATLAB21 hoursMATLAB is a numerical computing environment and programming language developed by MathWorks.
mlrobot1Machine Learning for Robotics21 hoursThis course introduces machine learning methods in robotics applications.

It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.

After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
bspkamlMachine Learning21 hoursThis course will be a combination of theory and practical work with specific examples used throughout the event.
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.
MLFWR1Machine Learning Fundamentals with R14 hoursThe aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform 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.
mlfunpythonMachine Learning Fundamentals with Python14 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.
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.
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
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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Excel VBA Introduction Belfast City Centre Mon, 2018-09-03 09:30 £2178 / £2678
Introduction to Selenium York - Priory Street Centre Tue, 2018-09-04 09:30 £1089 / £1239
Minitab for Statistical Data Analysis Cambridge Mon, 2018-09-10 09:30 £2574 / £3024
AWS: A Hands-on Introduction to Cloud Computing Edinburgh Training and Conference Venue Tue, 2018-09-11 09:30 £1287 / £1487
JMeter Fundamentals and JMeter Advanced Birmingham Tue, 2018-09-18 09:30 £2178 / £2828
Test Automation with Selenium St Helier, Jersey, Channel Isles Tue, 2018-09-18 09:30 £2970 / £4395
Jenkins: Continuous Integration for Agile Development Manchester, King Street Thu, 2018-10-18 09:30 £2574 / £3224
CakePHP: Rapid Web Application Development Birmingham Tue, 2018-11-06 09:30 £4356 / £5656

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