Machine Learning Training in Cardiff

Machine Learning Training in Cardiff

Local, instructor-led live Machine Learning (ML) 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 programmeming 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". Cardiff onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centres. 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]
mlfwr1Machine Learning Fundamentals with RCardiff14 hoursMon, 2018-11-26 09:30£2600 / £3200
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
mlintroIntroduction to Machine LearningCardiff7 hoursTue, 2018-12-11 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
mllbgMachine Learning in business – AI/RoboticsCardiff14 hoursThu, 2018-12-06 09:30£2600 / £3200
dlvDeep Learning for VisionCardiff21 hoursMon, 2018-12-03 09:30£3900 / £4800
octnpOctave not only for programmersCardiff21 hoursMon, 2018-12-10 09:30£3300 / £4200
mlfsasMachine Learning Fundamentals with Scala and Apache SparkCardiff14 hoursThu, 2018-12-13 09:30£2600 / £3200
dladvAdvanced Deep LearningCardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
tfirTensorFlow for Image RecognitionCardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
mlbankingrMachine Learning for Banking (with R)Cardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
encogadvEncog: Advanced Machine LearningCardiff14 hoursTue, 2018-12-11 09:30£2600 / £3200
embeddingprojectorEmbedding Projector: Visualizing Your Training DataCardiff14 hoursThu, 2018-12-13 09:30£2200 / £2800
intelligentmobileappsBuilding Intelligent Mobile ApplicationsCardiff35 hoursMon, 2018-12-10 09:30£5500 / £7000
dlfinancewithrDeep Learning for Finance (with R)Cardiff28 hoursTue, 2018-12-11 09:30£5200 / £6400
opennlpOpenNLP for Text Based Machine LearningCardiff14 hoursWed, 2018-12-05 09:30£2600 / £3200
t2tT2T: Creating Sequence to Sequence Models for Generalized LearningCardiff7 hoursThu, 2018-12-13 09:30£1100 / £1400
mlrobot1Machine Learning for RoboticsCardiff21 hoursTue, 2018-12-11 09:30£3900 / £4800
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
encogintroEncog: Introduction to Machine LearningCardiff14 hoursMon, 2018-12-17 09:30£2600 / £3200
mliosMachine Learning on iOSCardiff14 hoursTue, 2018-12-11 09:30£2600 / £3200
w2vdl4jNLP with Deeplearning4jCardiff14 hoursTue, 2018-12-18 09:30£2600 / £3200
matlabdlMatlab for Deep LearningCardiff14 hoursTue, 2018-12-18 09:30£2600 / £3200
mlfinancepythonMachine Learning for Finance (with Python)Cardiff21 hoursWed, 2018-12-05 09:30£3900 / £4800
aiautoArtificial Intelligence in AutomotiveCardiff14 hoursTue, 2018-12-18 09:30£2600 / £3200
pythontextmlPython: Machine Learning with TextCardiff21 hoursTue, 2018-12-18 09:30£3900 / £4800

Machine Learning Course Outlines in Cardiff

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.
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
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.
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.
mliosMachine Learning on iOS14 hoursIn this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app.

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

- Create a mobile app capable of image processing, text analysis and speech recognition
- Access pre-trained ML models for integration into iOS apps
- Create a custom ML model
- Add Siri Voice support to iOS apps
- Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
- Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder

Audience

- Developers

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
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.
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.
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.
mlfinancerMachine 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. 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 apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.

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.

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

- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
mlfinancepythonMachine Learning for Finance (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 finance 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.

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

- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and 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
mldtMachine Learning and Deep Learning21 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning)
mlbankingrMachine Learning for Banking (with R)28 hoursIn 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. R will be used as the programming language.

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 live projects.

Audience

- Developers
- Data scientists
- Banking professionals with a technical background

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
matlabml1Introduction to Machine Learning with MATLAB21 hoursMATLAB is a numerical computing environment and programming language developed by MathWorks.
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.
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.
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
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
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.
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
cortanaTurning Data into Intelligent Action with Cortana Intelligence28 hoursCortana Intelligence Suite is a bundle of integrated products and services on the Microsoft Azure Cloud that enable entities to transform data into intelligent actions.

In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications.

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

- Learn how to use Cortana Intelligence Suite tools
- Acquire the latest knowledge of data management and analytics
- Use Cortana components to turn data into intelligent action
- Use Cortana to build applications from scratch and launch it on the cloud

Audience

- Data scientists
- Programmers
- Developers
- Managers
- Architects

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.
bspkamlMachine Learning21 hoursThis course will be a combination of theory and practical work with specific examples used throughout the event.
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.
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.
aiintrozeroFrom Zero to AI35 hoursThis course is created for people who have no previous experience in probability and statistics.
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
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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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