Artificial Intelligence (AI) Training in Luxembourg City
Online or onsite, instructor-led live Artificial Intelligence (AI) training courses demonstrate through hands-on practice how to implement AI solutions for solving real-world problems.
AI training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Artificial Intelligence (AI) trainings in Luxembourg City can be carried out locally on customer premises or in NobleProg corporate training centers.
Learn Artificial Intelligence (AI) in our training center in Luxembourg City. The financial center and the touristic sites are really close.
Easy access to The EU-district and the International Airport Findel-Luxembourg by public buses. Read more
Testimonials
★★★★★
★★★★★
There were many exercises and interesting topics.
L M ERICSSON LIMITED
Course: Machine Learning
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
The Jupyter notebook form, in which the training material is available
L M ERICSSON LIMITED
Course: Machine Learning
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course: Neural Network in R
Very flexible.
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course: Python for Advanced Machine Learning
I generally enjoyed the flexibility.
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Course: Neural Network in R
The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Course: Machine Learning and Deep Learning
I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
I really enjoyed the coverage and depth of topics.
Anirban Basu
Course: Machine Learning and Deep Learning
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Course: Machine Learning
I genuinely liked excercises
L M ERICSSON LIMITED
Course: Machine Learning
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
The deep knowledge of the trainer about the topic.
Sebastian Görg
Course: Introduction to Deep Learning
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
Course: From Data to Decision with Big Data and Predictive Analytics
This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
I liked the new insights in deep machine learning.
Josip Arneric
Course: Neural Network in R
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course: TensorFlow for Image Recognition
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group
Course: From Data to Decision with Big Data and Predictive Analytics
This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
This 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.
This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
This instructor-led, live training in Luxembourg City (online or onsite) is aimed at technical persons who wish to set up or extend an RPA system with more intelligent capabilities.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA. - Enable robots to manage other robots. - Apply computer vision to locate screen objects with accuracy. - Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
This instructor-led, live training in Luxembourg City (online or onsite) is aimed at software testers who wish to have an AI driven software testing environment.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI. - Apply machine learning learning in a real world use-case. - Automate the generation and maintenance of API tests with AI. - Use machine learning methods to self-heal the execution of Selenium tests.
This instructor-led, live training in Luxembourg City (online or onsite) is aimed at marketers who wish to use AI to improve improve digital marketing strategies through valuable customer insights.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users. - Use chatbots to optimize the user-experience. - Increase productivity and revenue through the automation of tasks.
This instructor-led, live training in Luxembourg City (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data. - Unify the collection, organization and analysis of data. - Integrate Cloud Pak for Data with a variety of services to solve common business problems. - Implement workflows for collaborating with team members on the development of an AI solution.
This instructor-led, live training in Luxembourg City (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment. - Implement search algorithms and motion planning. - Implement PID controls to regulate a robot's movement within an environment. - Implement SLAM algorithms to enable a robot to map out an unknown environment.
This instructor-led, live training in Luxembourg City (online or onsite) is aimed at managers and business leaders who wish to learn about the fundamentals of artificial intelligence and manage AI projects for their organization.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
In this instructor-led, live training in Luxembourg City (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies. - Understand and manage the interaction between software and hardware in a robotic system. - Understand and implement the software components that underpin robotics. - Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice. - Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot. - Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment. - Implement search algorithms and motion planning. - Implement PID controls to regulate a robot's movement within an environment. - Implement SLAM algorithms to enable a robot to map out an unknown environment. - Test and troubleshoot a robot in realistic scenarios.
In this instructor-led, live training in Luxembourg City (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies. - Understand and manage the interaction between software and hardware in a robotic system. - Understand and implement the software components that underpin robotics. - Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice. - Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot. - Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment. - Implement search algorithms and motion planning. - Implement PID controls to regulate a robot's movement within an environment. - Implement SLAM algorithms to enable a robot to map out an unknown environment. - Extend a robot's ability to perform complex tasks through Deep Learning. - Test and troubleshoot a robot in realistic scenarios.
This 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.
Artificial 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.
Artificial 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.
This 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.
This 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.
This instructor-led, live 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.
Artificial 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.
In this instructor-led, live training in Luxembourg City, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data. - Use TPUs to speed up the inference process by up to two orders of magnitude. - Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
Microsoft 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.
PaddlePaddle (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
Snorkel 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
Encog 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
Encog 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
In 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
Online Artificial Intelligence (AI) training in Luxembourg City, AI training courses in Luxembourg City, Weekend Artificial Intelligence courses in Luxembourg City, Evening Artificial Intelligence (AI) training in Luxembourg City, Artificial Intelligence (AI) instructor-led in Luxembourg City, Artificial Intelligence (AI) on-site in Luxembourg City, Online Artificial Intelligence training in Luxembourg City, Evening Artificial Intelligence courses in Luxembourg City, Artificial Intelligence trainer in Luxembourg City, AI (Artificial Intelligence) one on one training in Luxembourg City, Artificial Intelligence instructor-led in Luxembourg City, Artificial Intelligence (AI) instructor in Luxembourg City, AI (Artificial Intelligence) coaching in Luxembourg City, Artificial Intelligence (AI) boot camp in Luxembourg City, Weekend Artificial Intelligence (AI) training in Luxembourg City, AI (Artificial Intelligence) classes in Luxembourg City, AI (Artificial Intelligence) private courses in Luxembourg City
We are looking for a good mixture of IT and soft skills in Luxembourg!
As a NobleProg Trainer you will be responsible for:
delivering training and consultancy Worldwide
preparing training materials
creating new courses outlines
delivering consultancy
quality management
At the moment we are focusing on the following areas:
Statistic, Forecasting, Big Data Analysis, Data Mining, Evolution Alogrithm, Natural Language Processing, Machine Learning (recommender system, neural networks .etc...)
SOA, BPM, BPMN
Hibernate/Spring, Scala, Spark, jBPM, Drools
R, Python
Mobile Development (iOS, Android)
LAMP, Drupal, Mediawiki, Symfony, MEAN, jQuery
You need to have patience and ability to explain to non-technical people
To apply, please create your trainer-profile by going to the link below:
.png "new york stock exchange (nyse)")
_ireland.gif "environmental protection agency (epa) ireland")
.png "aeronautical radio, incorporated (arinc)"){kind=small}
.jpg "european organization for nuclear research (cern)")
.png "european space agency (esa)")
