Artificial Intelligence (AI) Course Outlines in the UAE

Total Courses 195


 

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

 
Artificial Intelligence (AI)

Artificial Intelligence (AI)

What is Artificial Intelligence (AI)?

Genetic Algorithms

  28 hours

AI in business and Society & The future of AI - AI/Robotics

  7 hours

Intelligent Testing

  14 hours

Artificial Intelligence (AI) for Managers

  7 hours

UiPath for Intelligent Process Automation (IPA)

  14 hours

 

Introduction to Data Science and AI using Python

  35 hours

Big Data Business Intelligence for Telecom and Communication Service Providers

  35 hours

 

AI in Digital Marketing

  7 hours

AI-100: Designing & Implementing Azure AI Solutions- AI-100T01-A

  28 hours

AI-900T00: Microsoft Azure AI Fundamentals

  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

NLP: Natural Language Processing with R

  21 hours

Embedding Projector: Visualizing Your Training Data

  14 hours

Artificial Intelligence (AI) for Robotics

  21 hours

AI and Robotics for Nuclear

  80 hours

AI and Robotics for Nuclear - Extended

  120 hours

Natural Language Processing - AI/Robotics

  21 hours

Machine Learning for Robotics

  21 hours

 

AI-102T00: Designing and Implementing a Microsoft Azure AI Solution

  28 hours

 
Predictive Analytics

Predictive Analytics

What is Predictive Analytics?

From Data to Decision with Big Data and Predictive Analytics

  21 hours

Big Data Business Intelligence for Criminal Intelligence Analysis

  35 hours

Matlab for Predictive Analytics

  21 hours

Predictive Modelling with R

  14 hours

Introduction to R with Time Series Analysis

  21 hours

Visual Analytics – Data science

  14 hours

 

Machine Learning and Big Data

  7 hours

From Zero to AI

  35 hours

Machine Learning for Banking (with R)

  28 hours

Machine Learning for Banking (with Python)

  21 hours

Machine Learning for Finance (with Python)

  21 hours

Machine Learning for Finance (with R)

  28 hours

Machine Learning on iOS

  14 hours

MATLAB与机器学习入门

  21 hours

Artificial Intelligence Overview

  7 hours

Applied Machine Learning

  14 hours

Snorkel: Rapidly Process Training Data

  7 hours

Encog: Advanced Machine Learning

  14 hours

Encog: Introduction to 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

OpenNLP for Text Based Machine Learning

  14 hours

Text Summarization with Python

  14 hours

Machine Learning with Python – 2 Days

  14 hours

Machine Learning with Python – 4 Days

  28 hours

GANs and Variational Autoencoders in Python

  14 hours

Fundamentals of Artificial Intelligence and Machine Learning

  28 hours

Machine Learning Fundamentals with R

  14 hours

Data Mining & Machine Learning with R

  14 hours

Introduction to Machine Learning

  7 hours

Machine Learning

  21 hours

Machine Learning Concepts for Entrepreneurs and Managers

  21 hours

Machine Learning in business – AI/Robotics

  14 hours

Machine Learning – Data science

  21 hours

Algebra for Machine Learning

  14 hours

Artificial Intelligence for City Planning

  14 hours

Feature Engineering for Machine Learning

  14 hours

XGBoost for Gradient Boosting

  14 hours

Machine Learning with Python - Micro Learning

  4 hours

Dataiku for Enterprise AI and Machine Learning

  21 hours

DeepMind Lab

  14 hours

Vertex AI

  7 hours

 

Deep Learning for Finance (with R)

  28 hours

Deep Learning for Banking (with Python)

  28 hours

Deep Learning for Banking (with R)

  28 hours

Deep Learning for Finance (with Python)

  28 hours

Matlab for Deep Learning

  14 hours

Artificial Neural Networks, Machine Learning, Deep Thinking

  21 hours

Artificial Neural Networks, Machine Learning and Deep Thinking

  21 hours

Introduction Deep Learning and Neural Network for Engineers

  21 hours

Microsoft Cognitive Toolkit 2.x

  21 hours

Deep Reinforcement Learning with Python

  21 hours

Neural computing – Data science

  14 hours

Advanced Machine Learning with Python

  21 hours

Advanced Machine Learning with R

  21 hours

Amazon DSSTNE: Build a Recommendation System

  7 hours

Introduction to Deep Learning

  21 hours

Advanced Deep Learning

  28 hours

Machine Learning and Deep Learning

  21 hours

T2T: Creating Sequence to Sequence Models for Generalized Learning

  7 hours

Deep Learning AI Techniques for Executives, Developers and Managers

  21 hours

Deep Learning for Medicine

  14 hours

Deep Learning for Healthcare

  14 hours

Deep Learning for Business

  14 hours

 
Reinforcement Learning

Reinforcement Learning

What is Reinforcement Learning?

Reinforcement Learning with Java

  21 hours

Fundamentals of Reinforcement Learning

  21 hours

OpenAI Gym

  7 hours

Torch for Machine and Deep Learning

  21 hours

 

Octave not only for programmers

  21 hours

 

Introduction to the use of neural networks

  7 hours

 

Neural Network in R

  14 hours

 

Artificial Intelligence in Automotive

  14 hours

 

Neural Networks Fundamentals using TensorFlow as Example

  28 hours

TPU Programming: Building Neural Network Applications on Tensor Processing Units

  7 hours

Understanding Deep Neural Networks

  35 hours

Applied AI from Scratch

  28 hours

Natural Language Processing with TensorFlow

  35 hours

Deep Learning for NLP (Natural Language Processing)

  28 hours

Fraud Detection with Python and TensorFlow

  14 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

Pattern Recognition

What is Pattern Recognition?

Pattern Recognition

  21 hours

 

PaddlePaddle

  21 hours

Pattern Matching

  14 hours

Fiji: Introduction to Scientific Image Processing

  21 hours

 

Artificial Intelligence for Mechatronics

  21 hours

Deep Learning Neural Networks with Chainer

  14 hours

 

Natural Language Processing

  21 hours

Natural Language Processing with Python

  28 hours

Natural Language Processing with Deep Dive in Python and NLTK

  35 hours

 

NLP with Deeplearning4j

  14 hours

Mastering Deeplearning4j

  21 hours

DeepLearning4J for Image Recognition

  21 hours

 

Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

  21 hours

Python for Natural Language Generation

  21 hours

Natural Language Processing (NLP) with Python spaCy

  14 hours

 
Robotic Process Automation (RPA)

Robotic Process Automation (RPA)

What is Robotic Process Automation (RPA)?

Building Chatbots in Python

  21 hours

 

Computer Vision with Python

  14 hours

Deep Learning for Vision with Caffe

  21 hours

Computer Vision with SimpleCV

  14 hours

Real-Time Object Detection with YOLO

  7 hours

Deep Learning for Telecom (with Python)

  28 hours

AI Awareness for Telecom

  14 hours

Advanced Deep Learning with Keras and Python

  14 hours

Deep Learning for Self Driving Cars

  21 hours

Deep Learning with Keras

  21 hours

Python and Deep Learning with OpenCV 4

  14 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

Data Science: Analysis and Presentation

  7 hours

Scilab

  14 hours

Data Mining with Weka

  14 hours

 

RapidMiner for Machine Learning and Predictive Analytics

  14 hours

Artificial Intelligence (AI) with H2O

  14 hours

DataRobot

  7 hours

Machine Learning Fundamentals with Scala and Apache Spark

  14 hours

 

Machine Learning with PredictionIO

  21 hours

Turning Data into Intelligent Action with Cortana Intelligence

  28 hours

Kubeflow

  35 hours

Kubeflow Fundamentals

  28 hours

 

Core ML for iOS App Development

  14 hours

 

Mathematica for Machine Learning

  14 hours

Machine Learning with Python and Pandas

  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)

Amazon Web Services (AWS)

What is Amazon Web Services (AWS)?

Amazon Web Services (AWS) SageMaker

  21 hours

 

Practical Quantum Computing

  10 hours

Quantum Computing with IBM Quantum Experience

  14 hours

Fundamentals of Quantum Computing and Quantum Physics

  21 hours

Scaling Data Pipelines with Spark NLP

  14 hours

Insurtech: A Practical Introduction for Managers

  14 hours

OpenNN: Implementing Neural Networks

  14 hours

OpenNMT: Setting Up a Neural Machine Translation System

  7 hours

 

Facebook NMT: Setting up a Neural Machine Translation System

  7 hours

 

OpenFace: Creating Facial Recognition Systems

  14 hours

Hardware-Accelerated Video Analytics

  14 hours

Using Computer Network ToolKit (CNTK)

  28 hours

 

Apache SystemML for Machine Learning

  14 hours

 

Raspberry Pi + OpenCV for Facial Recognition

  21 hours

 

Mastering Apache SINGA

  21 hours

Marvin Framework for Image and Video Processing

  14 hours

 

Sphinx: Developing Speech-Enabled Applications

  21 hours

 
Cognitive Computing

Cognitive Computing

What is Cognitive Computing?

Cognitive Computing: An Introduction for Business Managers

  7 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

 

Quantum Computing with Cirq Framework

  21 hours

Getting Started with Quantum Computing and Q#

  14 hours

 
Microsoft Bot Framework

Microsoft Bot Framework

What is Microsoft Bot Framework?

Microsoft Bot Framework Fundamentals

  14 hours

Microsoft Bot Framework Composer

  14 hours

 

Building Deep Learning Models with Apache MXNet

  21 hours

Accelerating Deep Learning with FPGA and OpenVINO

  35 hours

Distributed Deep Learning with Horovod

  7 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

 

Machine Learning with Random Forest

  14 hours

AdaBoost Python for Machine Learning

  14 hours

AlphaFold

  7 hours

Artificial Intelligence (AI) Courses Testimonials

Total Feedbacks 50


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



Very flexible.

Frank Ueltzhöffer

Artificial Neural Networks, Machine Learning and Deep Thinking



I generally enjoyed the flexibility.

Werner Philipp

Artificial Neural Networks, Machine Learning and Deep Thinking



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



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







Other regions in the UAE