Artificial Intelligence (AI) Training Courses - United Arab Emirates
(50 feedbacks)
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 United Arab Emirates can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Subcategories (19)
Genetic Algorithms
28 hours
Intelligent Testing
14 hours
ChatGPT for Developers
35 hours
TensorFlow Lite for Android
21 hours
TensorFlow Lite for iOS
21 hours
TensorFlow Lite for Embedded Linux
21 hours
Tensorflow Lite for Microcontrollers
21 hours
OptaPlanner in Practice
21 hours
AI in Digital Marketing
7 hours
Azure Machine Learning
14 hours
MLOps for Azure Machine Learning
14 hours
Azure Machine Learning (AML)
21 hours
IBM Cloud Pak for Data
14 hours
AI and Robotics for Nuclear
80 hours
AI and Robotics for Nuclear - Extended
120 hours
Machine Learning for Robotics
21 hours
Matlab for Predictive Analytics
21 hours
Predictive Modelling with R
14 hours
Visual Analytics – Data science
14 hours
Machine Learning and Big Data
7 hours
From Zero to AI
35 hours
Machine Learning on iOS
14 hours
Applied Machine Learning
14 hours
Encog: Advanced Machine Learning
14 hours
Applied AI from Scratch in Python
28 hours
Recommender Systems with Python
14 hours
Python: Machine Learning with Text
21 hours
Text Summarization with Python
14 hours
Machine Learning Fundamentals with R
14 hours
Introduction to Machine Learning
7 hours
Machine Learning
21 hours
Machine Learning – Data science
21 hours
Algebra for Machine Learning
14 hours
XGBoost for Gradient Boosting
14 hours
DeepMind Lab
14 hours
Vertex AI
7 hours
LightGBM for Machine Learning
21 hours
Deep Learning for Finance (with R)
28 hours
Deep Learning for Banking (with R)
28 hours
Matlab for Deep Learning
14 hours
Microsoft Cognitive Toolkit 2.x
21 hours
Neural computing – Data science
14 hours
Advanced Machine Learning with R
21 hours
Introduction to Deep Learning
21 hours
Advanced Deep Learning
28 hours
Machine Learning and Deep Learning
21 hours
Deep Learning for Medicine
14 hours
Deep Learning for Healthcare
14 hours
Deep Learning for Business
14 hours
DeepSpeed for Deep Learning
21 hours
Reinforcement Learning with Java
21 hours
Octave not only for programmers
21 hours
Neural Network in R
14 hours
Understanding Deep Neural Networks
35 hours
Applied AI from Scratch
28 hours
Deep Learning for Vision
21 hours
Deep Learning with TensorFlow
21 hours
TensorFlow for Image Recognition
28 hours
TensorFlow Serving
7 hours
Deep Learning with TensorFlow 2
21 hours
Machine Learning with TensorFlow.js
14 hours
TensorFlow Extended (TFX)
21 hours
Pattern Recognition
21 hours
PaddlePaddle
21 hours
Pattern Matching
14 hours
NLP with Deeplearning4j
14 hours
Mastering Deeplearning4j
21 hours
DeepLearning4J for Image Recognition
21 hours
Building Chatbots in Python
21 hours
OpenAI API
14 hours
OpenAI Gym
7 hours
Computer Vision with Python
14 hours
Deep Learning for Vision with Caffe
21 hours
Computer Vision with SimpleCV
14 hours
AI Awareness for Telecom
14 hours
Deep Learning for Self Driving Cars
21 hours
Deep Learning with Keras
21 hours
Computer Vision with OpenCV
28 hours
ParlAI for Conversational AI
14 hours
Chatbots for Developers
14 hours
NLP with Python and TextBlob
14 hours
Scilab
14 hours
Data Mining with Weka
14 hours
DataRobot
7 hours
Kubeflow
35 hours
Kubeflow Fundamentals
28 hours
Kubeflow on OpenShift
28 hours
Core ML for iOS App Development
14 hours
Mathematica for Machine Learning
14 hours
MLOps: CI/CD for Machine Learning
35 hours
MLflow
21 hours
Machine Learning and AI with ML.NET
21 hours
Amazon Web Services (AWS) SageMaker
21 hours
Practical Quantum Computing
10 hours
ProjectQ
7 hours
Machine Learning Algorithms in Julia
21 hours
OpenNN: Implementing Neural Networks
14 hours
Hardware-Accelerated Video Analytics
14 hours
Machine Learning with PredictionIO
21 hours
Apache SystemML for Machine Learning
14 hours
Mastering Apache SINGA
21 hours
Natural Language Processing (NLP)
21 hours
AutoML
14 hours
Google Cloud AutoML
7 hours
AutoML with Auto-Keras
14 hours
AutoML with Auto-sklearn
14 hours
H2O AutoML
14 hours
Microsoft Bot Framework Fundamentals
14 hours
Microsoft Bot Framework Composer
14 hours
Machine Learning with Random Forest
14 hours
AdaBoost Python for Machine Learning
14 hours
AlphaFold
7 hours
ChatGPT
14 hours
ChatGPT for Marketing
14 hours
ChatGPT for SEO
14 hours
ChatGPT for Executives
14 hours
ChatGPT for Finance
14 hours
ChatGPT for Customer Service
14 hours
ChatGPT for Content Creation
14 hours
ChatGPT for E-commerce
14 hours
ChatGPT for Social Media Management
14 hours
ChatGPT for Healthcare
14 hours
ChatGPT for Legal Services
14 hours
ChatGPT (GPT-4) for Developers
14 hours
ChatGPT for Banking
14 hours
ChatGPT for Accounting
14 hours
ChatGPT for Human Resources
14 hours
ChatGPT for Education and Training
14 hours
ChatGPT for Sales Enablement
14 hours
ChatGPT for Virtual Assistants
14 hours
ChatGPT for Market Research
14 hours
ChatGPT for Product Development
14 hours
ChatGPT for Automation
14 hours
ChatGPT Plugins
21 hours
Midjourney for Image Generation
14 hours
He was very informative and helpful.
Pratheep Ravy [Predictive Modelling with R]It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease [Artificial Neural Networks, Machine Learning, Deep Thinking]Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq [Introduction to the use of neural networks]The interactive part, tailored to our specific needs.
Thomas Stocker [Introduction to the use of neural networks]I did like the exercises.
Office for National Statistics [Natural Language Processing with Python]I genuinely enjoyed the hands-on approach.
Kevin De Cuyper [Computer Vision with OpenCV]The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama [Data Mining & Machine Learning with R]The topic is very interesting.
Wojciech Baranowski [Introduction to Deep Learning]Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.
Grzegorz Mianowski [Introduction to Deep Learning]Topic. Very interesting!.
Piotr [Introduction to Deep Learning]Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.
Dolby Poland Sp. z o.o. [Introduction to Deep Learning]I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.
Radek [Introduction to Deep Learning]The global overview of deep learning.
Bruno Charbonnier [Advanced Deep Learning]The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Alexandre GIRARD [Advanced Deep Learning]Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis [Advanced Deep Learning]I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus [Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple]I generally enjoyed the knowledgeable trainer.
Sridhar Voorakkara [Neural Networks Fundamentals using TensorFlow as Example]I was amazed at the standard of this class - I would say that it was university standard.
David Relihan [Neural Networks Fundamentals using TensorFlow as Example]Very good all round overview. Good background into why Tensorflow operates as it does.
Kieran Conboy [Neural Networks Fundamentals using TensorFlow as Example]I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane [Neural Networks Fundamentals using TensorFlow as Example]We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.
Sebastiaan Holman [Machine Learning and Deep Learning]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 [Machine Learning and Deep Learning]I really enjoyed the coverage and depth of topics.
Anirban Basu [Machine Learning and Deep Learning]I liked the new insights in deep machine learning.
Josip Arneric [Neural Network in R]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 [Neural Network in R]I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb [Neural Network in R]The deep knowledge of the trainer about the topic.
Sebastian Görg [Introduction to Deep Learning]Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee [TensorFlow for Image Recognition]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 [Neural Networks Fundamentals using TensorFlow as Example]I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG [Neural Networks Fundamentals using TensorFlow as Example]In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall [Python for Advanced Machine Learning]This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn [Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP]Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group [From Data to Decision with Big Data and Predictive Analytics]The content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group [From Data to Decision with Big Data and Predictive Analytics]I genuinely liked excercises
- L M ERICSSON LIMITED [Machine Learning]I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED [Machine Learning]The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED [Machine Learning]There were many exercises and interesting topics.
- L M ERICSSON LIMITED [Machine Learning]Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
- L M ERICSSON LIMITED [Machine Learning]It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED [Machine Learning]The easy use of the VideoCapture functionality to acquire video images from laptop camera.
- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.
- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.
- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]It was easy to follow.
- HP Printing and Computing Solutions, Sociedad Limitada Unipe [Computer Vision with OpenCV]the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge [Introduction to R with Time Series Analysis]I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI [Advanced Deep Learning]About face area.
- 中移物联网 [Deep Learning for NLP (Natural Language Processing)]The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore [Deep Reinforcement Learning with Python]This is one of the best quality online training I have ever taken in my 13 year career. Keep up the great work!.
[Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP]It is one on one. I can ask a lot of question and also ask the trainner to repeat when I was not clear about some stuff.
[Insurtech: A Practical Introduction for Managers]