Artificial Intelligence (AI) Training Courses in the United Arab Emirates

This instructor-led, live training in UAE (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.

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

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow, machine learning and deep learning.
• Load TensorFlow Models onto an Android device.
• Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

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

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow and machine learning on mobile devices.
• Load TensorFlow Models onto an iOS device.
• Run an iOS application capable of detecting and classifying an object captured through the device's camera.

This instructor-led, live training in UAE (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

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

• Install and configure Tensorflow Lite on an embedded device.
• Understand the concepts and components underlying TensorFlow Lite.
• Convert existing models to TensorFlow Lite format for execution on embedded devices.
• Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
• Deploy a deep learning model on an embedded device running Linux.

This instructor-led, live training in UAE (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

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 is a classroom based training session in a presentation and Q&A format

This instructor-led, live training in UAE (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 UAE (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.

This instructor-led, live training in UAE (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 is a 5 day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python 

Overview

Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month.

Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored.

With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.)

This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain.

Course objectives

Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:

• Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
• How Big Data analytic differs from legacy data analytic
• In-house justification of Big Data -Telco perspective
• Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
• How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
• Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
• Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
• Network failure and service failure analytics from Network meta-data and IPDR
• Financial analysis-fraud, wastage and ROI estimation from sales and operational data
• Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
• Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
• Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization

Target Audience

• Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
• Business Analysts in Telco
• CFO office managers/analysts
• Operational managers
• QA managers

This instructor-led, live training in UAE (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.

Microsoft Azure Cloud offerings are the most comprehensive set of services w.r.t. AI, ML, DL

From Vision, Speech to Language to Conversational bots, Azure is empowering organizations to process their data to derive insights

This instructor-led, live training (online or onsite) is aimed at AI enthusiasts who wish to use Azure to build AI scenarios in the cloud

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

• Build Azure based AI scenarios.
• Understand the end to end functioning of API based infering
• Build conversational bots for business needs

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request more details or customized training for this course, please contact us to arrange.

This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis.

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

• Build machine learning models with zero programming experience.
• Create predictive algorithms with Azure Machine Learning.
• Deploy production ready machine learning algorithms.

This instructor-led, live training in UAE (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.

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

• Build reproducible workflows and machine learning models.
• Manage the machine learning lifecycle.
• Track and report model version history, assets, and more.
• Deploy production ready machine learning models anywhere.

This instructor-led, live training in UAE (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.

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

• Write highly-accurate machine learning models using Python, R, or zero-code tools.
• Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
• Use Azures interactive workspace to collaboratively develop ML models.
• Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.

This instructor-led, live training in UAE (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.

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

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

• Explore how data is being interpreted by machine learning models
• Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
• Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
• Explore the properties of a specific embedding to understand the behavior of a model
• Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in UAE (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.

In this instructor-led, live training in UAE (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 UAE (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 classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python

This 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.

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.

Audience Profile

Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.

Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run, so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another.

In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
• Implement industrial big data storage and processing solutions for data analysis
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation

Audience

• Law Enforcement specialists with a technical background

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

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

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

R 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.

R 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.

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake.

This instructor-led, live training in UAE (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.

By the end of this training, participants will:

• Understand the evolution and trends for machine learning.
• Know how machine learning is being used across different industries.
• Become familiar with the tools, skills and services available to implement machine learning within an organization.
• Understand how machine learning can be used to enhance data mining and analysis.
• Learn what a data middle backend is, and how it is being used by businesses.
• Understand the role that big data and intelligent applications are playing across industries.

This course is created for people who have no previous experience in probability and statistics.

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. 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

Machine 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

Machine 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

Machine 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

In 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

MATLAB is a numerical computing environment and programming language developed by MathWorks.

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 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.

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

This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems.

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

• Create recommender systems at scale.
• Apply collaborative filtering to build recommender systems.
• Use Apache Spark to compute recommender systems on clusters.
• Build a framework to test recommendation algorithms with Python.

In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.

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

• Solve text-based data science problems with high-quality, reusable code
• Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
• Build effective machine learning models using text-based data
• Create a dataset and extract features from unstructured text
• Visualize data with Matplotlib
• Build and evaluate models to gain insight
• Troubleshoot text encoding errors

Audience

• Developers
• Data Scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

The 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

In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.

In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.

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

• Use a command-line tool that summarizes text.
• Design and create Text Summarization code using Python libraries.
• Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17

Audience

• Developers
• Data Scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

The 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.

The aim of this course is to provide general 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.

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use GANs and variational autoencoders to generate new, synthetic instances of images, videos, and audio.

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

• Build a GAN using machine learning libraries in Python.
• Create a variational autoencoder with TensorFlow.
• Improve the training stability of GANs.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed.

Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning.
• Learn the applications and uses of deep learning in telecom.
• Use Python, Keras, and TensorFlow to create deep learning models for telecom.
• Build their own deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The 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.

R 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.

This 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.

This course will be a combination of theory and practical work with specific examples used throughout the event.

This 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

1. Investors and AI entrepreneurs
2. Managers and Engineers whose company is venturing into AI space
3. Business Analysts & Investors

This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge

This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.

Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster.

In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods.

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

• Understand fundamental linear algebra concepts
• Learn the linear algebra skills needed for machine learning
• Use linear algebra structures and concepts when working with data, images, algorithms, etc.
• Solve a machine learning problem using linear algebra

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?

In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.

Audience

• City planners
• Architects
• Developers
• Transportation officials

Format of the Course

• Part lecture, part discussion, and a series of interactive exercises.

Note

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in UAE (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models.

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

• Set up an optimal development environment, including all needed Python packages.
• Obtain important insights by analyzing the features of a data set.
• Optimize machine learning models through adaptation of the raw data itself.
• Clean and transform data sets in preparation for machine learning.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use XGBoost to build models that efficiently solve regression, classification, ranking, and prediction problems.

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

• Install and configure XGBoost.
• Understand the relationship between decision trees and other algorithms such as logistic regression and random forest.
• Test different libraries to determine the best one for the job.
• Choose the right configuration for an algorithm.
• Tune the hyper-parameters of an algorithm for a given dataset.
• Implement a machine learning solution that balances power with complexity, explainability, and ease of implementation.

This instructor-led, live online training is delivered as a micro learning event. Aimed at participants who wish to learning the specific aspects of classification and regression supervised machine learning.

By the end of the session, participants will be able to:

• Use machine learning to solve business problems
• Apply different machine learning and utilize the most robust one.
• Deploy classification and regression models.
• Finetune parameters to maximize the  utilization of the models

Format of the Course

• Micro learning
• Intensive and focused training on the core subject
• Interactive lecture and discussion.
• Hands-on implementation in a live-lab environment.

This instructor-led, live training in UAE (online or onsite) is aimed at engineers, data scientists, and data analysts who wish to use Dataiku DSS for the development of machine learning pipelines, and leverage AI processes in generating strategic organizational initiatives.

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

• Install and configure Dataiku DSS upon their preferred operating system.
• Understand AI/ML concepts and principles that are fundamental to Dataiku features.
• Create and implement their own data visualization codes in Dataiku DSS projects.
• Deploy ML models and pipelines into production environments built around Dataiku DSS.
• Optimize the agility and flexibility of data analysis methods of their enterprise applications.
• Utilize Dataiku DSS to secure and manage how data flows through further business systems.

This instructor-led, live training in UAE (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.

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

• Customize DeepMind Lab to build and run an environment that suits learning and training needs.
• Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

This instructor-led, live training in UAE (online or onsite) is aimed at software engineers or anyone who wish to learn how to use Vertex AI to perform and complete machine learning activities.

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

• Understand how Vertex AI works and use it as a machine learning platform.
• Learn about machine learning and NLP concepts.
• Know how to train and deploy machine learning models using Vertex AI.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. 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 implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

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

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use R to create deep learning models for finance
• Build their own deep learning stock price prediction model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use Python, Keras, and TensorFlow to create deep learning models for banking
• Build their own deep learning credit risk model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. 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 implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use R to create deep learning models for banking
• Build their own deep learning credit risk model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

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

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use Python, Keras, and TensorFlow to create deep learning models for finance
• Build their own deep learning stock price prediction model using Python

Audience

• Developers
• 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

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.

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.

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.

Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

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

• Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
• Apply advanced Reinforcement Learning algorithms to solve real-world problems
• Build a Deep Learning Agent

Audience

• Developers
• Data Scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries

In this instructor-led, live training in UAE, 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.

In 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:

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

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

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

This course covers AI (emphasizing Machine Learning and Deep Learning)

Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.

In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.

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

• Install tensor2tensor, select a data set, and train and evaluate an AI model
• Customize a development environment using the tools and components included in Tensor2Tensor
• Create and use a single model to concurrently learn a number of tasks from multiple domains
• Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
• Obtain satisfactory processing results using a single GPU

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Introduction:

Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.

This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.

Target Audience:

( Depending on target audience, course materials will be customized)

Executives

A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.

Project Managers

How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.

Developers

In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.

Salespersons

A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.

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

• Understand the fundamentals of Deep Learning
• Learn Deep Learning techniques and their applications in the industry
• Examine issues in medicine which can be solved by Deep Learning technologies
• Explore Deep Learning case studies in medicine
• Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

• Managers
• Medical professionals in leadership roles

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in UAE (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans.

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

• Install and configure the necessary development environment, software and libraries to begin developing.
• Analyze MRI images using deep learning techniques such as CNNs.
• Detect potential health conditions such as heart disease through MRI scan analysis.
• Apply techniques such as image segmentation and CNN training to identify potential disease.
• Identify the genomics of a disease using radiomics.
• Build and deploy a deep learning application aimed at healthcare image analysis.

This instructor-led, live training in UAE (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.

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

• Understand the core concepts of machine learning and deep learning.
• Get insights on the future of business and industry with ML and DL.
• Define business strategies and solutions with deep learning.
• Learn how to apply data science and deep learning in solving business problems.
• Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.

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

• Understand the relationships and differences  between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
• Analyze a real-world problem and redefine it as Reinforcement Learning problem.
• Implementing a solution to a real-world problem using Reinforcement Learning.
• Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.

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

• Install and apply the libraries and programming language needed to implement Reinforcement Learning.
• Create a software agent that is capable of learning through feedback instead of through supervised learning.
• Program an agent to solve problems where decision making is sequential and finite.
• Apply knowledge to design software that can learn in a way similar to how humans learn.

This instructor-led, live training in UAE (online or onsite) is aimed at researchers and developers who wish to install, configure, customize, and implement OpenAI Gym to quickly develop reinforcement learning algorithms.

By the end of this training, participants will be able to build, develop, execute, and test reinforcement learning algorithms to optimize tasks and achieve maximum results.

Torch 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 instructor-led, live training, 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.

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

Course 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.

The training is aimed at people who want to learn the basics of neural networks and their applications.

This course is an introduction to applying neural networks in real world problems using R-project software.

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.

In this instructor-led, live training in UAE, 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.

This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN

• understand TensorFlow’s structure and deployment mechanisms

• be able to carry out installation / production environment / architecture tasks and configuration

• be able to assess code quality, perform debugging, monitoring

• be able to implement advanced production like training models, building graphs and logging

This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. 

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, embedding terms, building graphs and logging

In this instructor-led, live training in UAE, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions. 

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

• Design and code DL for NLP using Python libraries.
• Create Python code that reads a substantially huge collection of pictures and generates keywords.
• Create Python Code that generates captions from the detected keywords.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images

This course provide working examples.

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging

This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

• understand TensorFlow’s structure and deployment mechanisms
• carry out installation / production environment / architecture tasks and configuration
• assess code quality, perform debugging, monitoring
• implement advanced production like training models, building graphs and logging

In this instructor-led, live training in UAE (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

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

• Train, export and serve various TensorFlow models.
• Test and deploy algorithms using a single architecture and set of APIs.
• Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.

This instructor-led, live training in UAE (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.

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

• Build and train machine learning models with TensorFlow.js.
• Run existing machine learning models in the browser or under Node.js.
• Retrain pre-existing machine learning using custom data.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.

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

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

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.

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

Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis.

In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application.

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

• Use Fiji's advanced programming features and software components to extend ImageJ
• Stitch large 3d images from overlapping tiles
• Automatically update a Fiji installation on startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets
• Deploy their application and collaborate with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in UAE (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.

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

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

This instructor-led, live training in UAE (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.

The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc...

For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.

This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.

By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.

Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Audience

This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.

After this course delegates will be able to:

Deeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark.

Audience

This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects.

This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.

Natural language generation (NLG) refers to the production of natural language text or speech by a computer.

In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.

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

• Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
• Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
• Understand the NLG pipeline and apply the right techniques at each stage
• Understand the architecture of a Natural Language Generation (NLG) system
• Implement the most suitable algorithms and models for analysis and ordering
• Pull data from publicly available data sources as well as curated databases to use as material for generated text
• Replace manual and laborious writing processes with computer-generated, automated content creation

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights.

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

• Install and configure spaCy.
• Understand spaCy's approach to Natural Language Processing (NLP).
• Extract patterns and obtain business insights from large-scale data sources.
• Integrate the spaCy library with existing web and legacy applications.
• Deploy spaCy to live production environments to predict human behavior.
• Use spaCy to pre-process text for Deep Learning

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.
• To learn more about spaCy, please visit: https://spacy.io/

ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions.

In this instructor-led, live training, participants will learn how to build chatbots in Python.

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

• Understand the fundamentals of building chatbots
• Build, test, deploy, and troubleshoot various chatbots using Python

Audience

• Developers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.

In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.

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

• Understand the basics of Computer Vision
• Use Python to implement Computer Vision tasks
• Build their own face, object, and motion detection systems

Audience

• Python programmers interested in Computer Vision

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

After completing this course, delegates will be able to:

• understand Caffe’s structure and deployment mechanisms
• carry out installation / production environment / architecture tasks and configuration
• assess code quality, perform debugging, monitoring
• implement advanced production like training models, implementing layers and logging

SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too.

Audience

This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.

This instructor-led, live training in UAE (online or onsite) is aimed at back-end developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object-detection.

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

• Install and configure the necessary tools and libraries required in object detection using YOLO.
• Customize Python command-line applications that operate based on YOLO pre-trained models.
• Implement the framework of pre-trained YOLO models for various computer vision projects.
• Convert existing datasets for object detection into YOLO format.
• Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.

In this instructor-led, live training in UAE, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning.
• Learn the applications and uses of deep learning in telecom.
• Use Python, Keras, and TensorFlow to create deep learning models for telecom.
• Build their own deep learning customer churn prediction model using Python.

AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.

In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.

Audience

• Network engineers
• Network operations personnel
• Telecom technical managers

Format of the course

• Part lecture, part discussion, hands-on exercises

This instructor-led, live training in UAE (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python.

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

• Apply deep learning with supervised or unsupervised learning methods.
• Develop, train, and implement concurrent neural networks and recurrent neural networks.
• Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.

This instructor-led, live training in UAE (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques.

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

• Use Keras to build and train a convolutional neural network.
• Use computer vision techniques to identify lanes in an autonomos driving project.
• Train a deep learning model to differentiate traffic signs.
• Simulate a fully autonomous car.

This instructor-led, live training in UAE (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

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

• Install and configure Keras.
• Quickly prototype deep learning models.
• Implement a convolutional network.
• Implement a recurrent network.
• Execute a deep learning model on both a CPU and GPU.

This instructor-led, live training in UAE (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects

This instructor-led, live training in UAE (online or onsite) is aimed at researchers and developers who wish to install, configure, customize, and manage the ParlAI platform to develop their AI models.

By the end of this training, participants will be able to share, train, and evaluate AI models to build and develop conversational solutions across existing chat services.

This instructor-led, live training in UAE (online or onsite) is aimed at developers who wish to install, set up, and manage chatbot-building platforms and code-based frameworks to create and deploy chatbot services.

By the end of this training, participants will be able to design, build, evaluate, and test chatbot services to provide automated chat or voice support interfaces for their users.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and developers who wish to use TextBlob to implement and simplify NLP tasks, such as sentiment analysis, spelling corrections, text classification modeling, etc.

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

• Set up the necessary development environment to start implementing NLP tasks with TextBlob.
• Understand the features, architecture, and advantages of TextBlob.
• Learn how to build text classification systems using TextBlob.
• Perform common NLP tasks (Tokenization, WordNet, Sentiment analysis, Spelling correction, etc.)
• Execute advanced implementations with simple APIs and a few lines of codes.

The 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.

Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems.

In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.

By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.

Audience

• Data scientists and engineers, especially with interest in image processing and facial recognition

Format of the course

• Part lecture, part discussion, exercises and intensive hands-on practice, with a final project

This instructor-led, live training in UAE (online or onsite) is aimed at data analysts and data scientists who wish to use Weka to perform data mining tasks.

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

• Install and configure Weka.
• Understand the Weka environment and workbench.
• Perform data mining tasks using Weka.

RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. 

In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment. 

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

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in UAE (online or onsite) is aimed at technical persons who wish to build machine learning models using algorithms such as GLM, Deep Learning and Random Forests.

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

• Install and configure H2O.
• Create machine learning models using different popular algorithms.
• Evaluate models based on the type of data and business requirements.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

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

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

The 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.

PredictionIO 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.

Cortana 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

This instructor-led, live training in UAE (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in UAE (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in UAE (online or onsite) is aimed at web developers who wish to create machine learning models with Core ML for iOS 11 and macOS High Sierra development.

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

• Build AI driven applications that use machine learning.
• Implement machine learning models that classify images.
• Use the Core ML API for supporting custom workflows and advanced use cases
• Analyze natural language text with machine learning models.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis.

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

• Build and train machine learning models.
• Import and prepare data for machine learning.
• Separate training data from test data.
• Explore deep learning and neural network applications in data analysis.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use Pandas to preform predictive analysis with machine learning.

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

• Perform data wrangling in Python.
• Conduct ETL operations for machine learning.
• Create data visualizations with Pandas

This instructor-led, live training in UAE (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

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

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducability and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

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

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments.

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

• Use notebook instances to prepare and upload data for training.
• Train machine learning models using training datasets.
• Deploy trained models to an endpoint to create predictions.

Practical Quantum Computing: Live Online

Launch your high-tech career

This is a 10 hour instructor-led, live online training course. After your immersive training, you will be ready to start work as an entry level quantum computing developer.

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

• Run and test your quantum programs with the integrated IBM Q
• Use Qiskit to create, compile, and execute quantum computing programs
• Working with practical and advanced quantum algorithms such as QAOA 
• Recast real-world problems into an appropriate quantum computing language

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in UAE (online or onsite) is aimed at computer scientists who wish to create, edit, and invoke a quantum circuit using IBM Quantum Experience.

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

• Set up the necessary development environment to start creating quantum circuits.
• Improve on inaccuracies in quibits using IBM Quantum Experience and its simulations.
• Control gate behavior using the gate's native counterpart.
• Schedule the time of gate events.

This instructor-led, live training in UAE (online or onsite) is aimed at computer scientists and engineers who wish to understand the principles behind quantum computing and utilize them in developing algorithms for quantum computer implementations.

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

• Comprehend the fundamentals of quantum computing.
• Understand and apply quantum physics concepts into computational methods.
• Create algorithms for quantum computers.
• Solve computational problems efficiently with quantum computers.
• Integrate quantum behaviors into existing computational models.
• Perceive the potential of quantum computing in the advancement of other technologies.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.

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

• Set up the necessary development environment to start building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Use the pre-trained models available in Spark NLP to implement text processing.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).

Insurtech (a.k.a Digital Insurance) refers to the convergence of insurance + new technologies. In the field of Insurtech "digital insurers" apply technology innovations to their business and operating models in order to reduce costs, improve the customer experience and enhance the agility of their operations.

In this instructor-led training, participants will gain an understanding of the technologies, methods and mindset needed to bring about a digital transformation within their organizations and in the industry at large. The training is aimed at managers who need to gain a big picture understanding, break down the hype and jargon, and take the first steps in establishing an Insurtech strategy.

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

• Discuss Insurtech and all its component parts intelligently and systematically
• Identify and demystify the role of each key technology within Insurtech.
• Draft a general strategy for implementing Insurtech within their organization

Audience

• Insurers
• Technologists within the insurance industry
• Insurance stakeholders
• Consultants and business analysts

Format of the course

• Part lecture, part discussion, exercises and case study group activities

In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.

Format of the course

• Lecture and discussion coupled with hands-on exercises.

In this instructor-led, live 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.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice

In this instructor-led, live training, participants will learn how to use Facebook NMT (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.

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.

OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.

In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.

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

• Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
• Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in UAE (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.

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

• Install and configure the necessary development environment, software and libraries to begin developing.
• Build, train, and deploy deep learning models to analyze live video feeds.
• Identify, track, segment and predict different objects within video frames.
• Optimize object detection and tracking models.
• Deploy an intelligent video analytics (IVA) application.

Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images.

Audience

This course is directed at engineers and architects aiming to utilize CNTK in their projects.

Apache 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.

This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.

The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.

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

• Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
• Configure OpenCV to capture and detect facial images.
• Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
• Adapt the system for a variety of use cases, including surveillance, identity verification, etc.

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.

After completing this course, delegates will:

• understand SINGA’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, embedding terms, building graphs and logging

Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java.  Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation.

Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection.

In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application.

Format of the Course

• The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.

Speech technology is increasingly being used to create highly interactive, voice-activated applications. From voice-control, to smart assistants, to speech transcription and translation, to closed-captioning and language learning, the improved accuracy and processing speed of this technology is enhancing the quality of applications and delivering greater user experiences.

In this course we use the open-source Sphinx toolkit (aka CMU Sphinx) to demonstrate and model various types of speech-enabled applications. By the end of the course participants should have a solid grasp of the tools and techniques needed to apply speech technology to their own applications. Sphinx 4 will be the basis for this training, however, coverage of Sphinx 3 can also be arranged.

Audience

• Software developers and programmers

Format of the course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Cognitive computing refers to systems that encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, to name a few. A cognitive computing system is often comprised of multiple technologies working together to process in-memory 'hot' contextual data as well as large sets of 'cold' historical data in batch. Examples of such technologies include Kafka, Spark, Elasticsearch, Cassandra, and Hadoop.

In this instructor-led, live training, participants will learn how Cognitive Computing compliments AI and Big Data and how purpose-built systems can be used to realize human-like behaviors that improve the performance of human-machine interactions in business.

By the end of this training, participants will understand:

• The relationship between cognitive computing and artificial intelligence (AI)
• The inherently probabilistic nature of cognitive computing and how to use it as a business advantage
• How to manage cognitive computing systems that behave in unexpected ways
• Which companies and software systems offer the most compelling cognitive computing solutions

Audience

• Business managers

Format of the course

• Lecture, case discussions and exercises

This instructor-led, live training in UAE (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.

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

• Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
• Train high quality machine learning models.
• Efficiently solve different types of supervised machine learning problems.
• Write just the necessary code to initiate the automated machine learning process.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product line to implement different services for various data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

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

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This instructor-led, live training in UAE (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

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

• Automate the process of training highly efficient machine learning models.
• Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
• Use the power of machine learning to solve real-world business problems.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.

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

• Automate the machine learning workflow.
• Automatically train and tune many machine learning models within a specified time range.
• Train stacked ensembles to arrive at highly predictive ensemble models.

In this instructor-led, live training in UAE, participants will learn the fundamentals of quantum computing as they step through the development of algorithms targeting quantum computers.

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

• Set up the necessary development environment to start creating quantum circuits.
• Control gate behavior using the gate's native counterpart.
• Improve on quibit inaccuracies using Cirq.
• Write and execute a quantum circuit to run against a NISQ based quantum computer simulator.

In this instructor-led, live training in UAE, participants will learn the fundamentals of quantum computing and Q# as they step through the development of simple quantum programs.

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

• Install and configure Microsoft's Quantum Development Kit.
• Understand the concepts behind quantum computing.
• Build, test, execute, and troubleshoot a quantum program using Q#, Visual Studio and a local quantum computing simulator.

This instructor-led, live training in UAE (online or onsite) is aimed at web developers who wish to build conversational AI experiences.

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

• Build guided bot conversations with Microsoft Bot Framework.
• Enhance bots with dialogs and FormFlow.
• Integrate Microsoft's NLP framework into a bot.
• Deploy a bot to one or multiple channels.

This instructor-led, live training in UAE (online or onsite) is aimed at web developers who wish to use Microsoft Bot Framework Composer to design, build, and test conversational AI applications.

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

• Set up the necessary development environment to start developing bot projects.
• Create a bot and integrate additional functionalities and elements with Composer.
• Enhance bot features with Composer API and customize interactions using CLI tools.
• Test, debug, and validate bots, and then deploy them to Azure.

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition.

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

• Install and configure Apache MXNet and its components.
• Understand MXNet's architecture and data structures.
• Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
• Build a convolutional neural network for image classification.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in UAE (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

This instructor-led, live training in UAE (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimized for processing on mobile devices.

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

• Set up the necessary development environment to start developing machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs. 
• Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

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

• Set up the necessary development environment to start building machine learning models with Random forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

This instructor-led, live training in UAE (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.

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

• Set up the necessary development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This instructor-led, live training in UAE (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.