Artificial Intelligence (AI) for Mechatronics Training Course


21 hours (usually 3 days including breaks)


  • Basic understanding of computer science and engineering


  • Engineers


Mechatronics (a.k.a. mechatronic engineering) is a combination of mechanical, electronics and computer science.

This instructor-led, live training (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.

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.

Course Outline


Overview of Artificial Intelligence (AI)

  • Machine learning
  • Computational intelligence

Understanding the Concepts of Neural Networks

  • Generative networks
  • Deep neural networks
  • Convolution neural networks

Understanding Various Learning Methods

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Semi-supervised learning

Other Computational Intelligence Algorithms

  • Fuzzy systems
  • Evolutionary algorithms

Exploring Artificial Intelligence Approaches to Optimization

  • Choosing AI Approaches Effectively

Learning about Stochastic Dynamic Programming

  • Relationship with AI

Implementing Mechatronic Applications with AI

  • Medicine
  • Rescue
  • Defense
  • Industry-agnostic trend

Case Study: The Intelligent Robotic Car

Programming the Major Systems of a Robot

  • Planning the Project

Implementing AI Capabilities

  • Searching and Motion Control
  • Localization and Mapping
  • Tracking and Controlling

Summary and Next Steps

Artificial Intelligence (AI) in Automotive Training Course


14 hours (usually 2 days including breaks)


The participants must have programming experience (any language) and engineering background, but are not required to write any code during the course.


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.

Course Outline

Current state of the technology

  • What is used
  • What may be potentially used

Rules based AI 

  • Simplifying decision

Machine Learning 

  • Classification
  • Clustering
  • Neural Networks
  • Types of Neural Networks
  • Presentation of working examples and discussion

Deep Learning

  • Basic vocabulary 
  • When to use Deep Learning, when not to
  • Estimating computational resources and cost
  • Very short theoretical background to Deep Neural Networks

Deep Learning in practice (mainly using TensorFlow)

  • Preparing Data
  • Choosing loss function
  • Choosing appropriate type on neural network
  • Accuracy vs speed and resources
  • Training neural network
  • Measuring efficiency and error

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS

From Zero to AI Training Course


35 hours (usually 5 days including breaks)


None. All concepts like probability and statistics will be explained during this course. If you are already familiar with probability and statistics, please refer to our course code aiint.


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

Course Outline

Probability (3.5h)

  • Definition of probability
  • Binomial distribution
  • Everyday usage exercises

Statistics (10.5h)

  • Descriptive Statistics
  • Inferential Statistics
  • Regression
  • Logistic Regression
  • Exercises

Introduction to Programming (3.5h)

  • Procedural Programming
  • Functional Programming
  • OOP Programming
  • Exercises (writing logic for a game of choice, e.g. noughts and crosses)

Machine Learning (10.5h)

  • Classification
  • Clustering
  • Neural Networks
  • Exercises (write AI for a computer game of choice)

Rules Engines and Expert Systems (7 hours)

  • Intro to Rule Engines
  • Write AI for the same game and combine solutions into hybrid approach

AI and Robotics for Nuclear Training Course


80 hours (usually 12 days including breaks)


  • Programming experience in C or C++
  • Programming experience in Python (useful but not necessary; can be taught as part of course)
  • Experience with Linux command line


  • Developers
  • Engineers
  • Scientists
  • Technicians


Robotics and Artificial Intelligence (AI) are powerful tools for the development of safety systems in nuclear facilities.

In this instructor-led, live training (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.

Format of the Course

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

About the Hardware

  • Hardware kits will be confirmed by the instructor before the training. Kits will more-or-less contain the following components:
    • Arduino board
    • Motor controller
    • Distance sensor
    • Bluetooth slave
    • Prototyping board and cables
    • USB cable
    • Vehicle kit
  • Participants will need to provision their own hardware.

Course Customization Options

  • To customize any part of this course (programming language, robot model, microcontroller, etc.) please contact us to arrange.

Course Outline

Week 01

Day 01


  • What Makes a Robot smart?

Physical vs Virtual Robots

  • Smart Robots, Smart Machines, Sentient Machines and Robotic Process Automation (RPA), etc.

The Role of Artificial Intelligence (AI) in Robotics

  • Beyond “if-then-else” and the learning machine
  • The algorithms behind AI
  • Machine learning, computer vision, natural language processing (NLP), etc.
  • Cognitive robotics

Day 02

The Role of Big Data in Robotics

  • Decision-making based on data and patterns

The Cloud and Robotics

  • Linking robotics with IT
  • Building more functional robots that access more information and collaborate

Case Study: Industrial Robots

  • Mechanical Robots
    • Baxter
  • Robots in Nuclear Facilities
    • Radiation detection and protection
  • Robots in Nuclear Reactors
    • Radiation detection and protection

Day 03

Hardware Components of a Robot

  • Motors, sensors, microcontrollers, cameras, etc.

Common Elements of Robots

  • Machine vision, voice recognition, speech synthesis, proximity sensing, pressure sensing, etc.

Day 04

Development Frameworks for Programming a Robot

  • Open source and commercial frameworks
  • Robot Operating System (ROS)
    • Architecture: workspace, topics, messages, services, nodes, actionlibs, tools, etc.

Languages for Programming a Robot

  • C++ for low level controlling
  • Python for orchestration
  • Programming ROS nodes in Python and C ++
  • Other languages

Day 05

Tools for Simulating a Physical Robot

  • Commercial and open source 3D simulation and visualization software

Tools for Designing the Physical Characteristics of a Robot

  • Commercial and open source CAD software

Case Study: Mechanical Robots

  • Robots in the nuclear technology field
  • Robots in environmental systems

Week 02

Day 06

Crash Course in Python

  • Software installation and setup
  • Useful packages and utilities
  • Working with Python data structures, operators, loops, conditionals, functions, methods, etc.
  • Writing a sample program
  • Team project

Day 07

Preparing for Robot Development

  • Setting up the development environment (e.g., Arduino IDE)
  • Exploring the Arduino language (C/C++) syntax
  • Coding, compiling, and uploading to the microcontroller
  • Assembling the hardware components of an Arduino robot

Day 08

Working with Arduino Components

  • Analog sensors
  • Digital sensors

Working with Arduino Communication Modules

  • Bluetooth Modules
  • Wi-Fi Modules
  • RFID Modules
  • I2C and SPI
  • Mobile internet

Day 09

Constructing a Robot

  • Planning the features and characteristics of a robot
  • Implementing robot movement

Team project

  • Discussion and review

Day 10

Controlling the Robot

  • Implementing the controller
  • Connecting to the robot (wired and wirelessly)

Team Project

  • Discussion and review

Week 03

Day 11

Programming the Robot

  • Simulating a robot with Gazebo / ROS
  • Understanding ROS node
  • Programming a node in Python and C ++
  • Messages and topics in ROS
  • Publication / subscription paradigm

Team Project

  • Bump & Go with real robot
  • Discussion and review

Day 12

Programming the Robot (continued…)

  • Frames in ROS and reference changes
  • 2D information processing of cameras with OpenCV
  • Information processing of a laser

Team Project

  • Safe tracking of objects by color
  • Discussion and review

Day 13

Testing the Robot

  • Tools for testing your code
  • Unit testing
  • Creating a test suite
  • Automating your tests
  • Troubleshooting

Team Project

  • Safe tracking of objects by color
  • Discussion and review

Day 14

Programming the Robot (Continued…)

  • Services in ROS
  • 3D information processing of RGB-D sensors with PCL
  • Maps and Navigation with ROS

Day 15

Programming the Robot (Continued…)

  • Completing tasks with ActionLib

Team Project

  • Search for objects in the environment

Week 04

Day 16

Programming the Robot (Continued…)

  • Completing tasks with ActionLib

Day 17

Programming the Robot (Continued…)

  • Speech Recognition and Speech Generation
  • Troubleshooting

Team Project

  • Controlling a robot using voice

Day 18

Programming the Robot (Continued…)

  • Controlling robotic arms with MoveIt!
  • Controlling robotic neck for active vision
  • Troubleshooting

Team Project

  • Search and collection of objects

Day 19

Deploying the Robot

  • Deploying the robot in the physical world
  • Monitoring and servicing robots in the field
  • Using a mobile app to control a robot

Securing the Robot

  • Preventing unauthorized tampering
  • Preventing hackers from viewing and stealing sensitive data

Day 20

Data Analytics

  • Collecting and organizing data generated by the robot
  • Making sense of the data through visualization tools and processes

Building a Robot Collaboratively

  • Building a robot in the cloud
  • Building a mobile app to interact with your robot
  • Joining the robotics community

Future Outlook for Robots in the Science and Energy Field

Summary and Conclusion

AI in Digital Marketing Training Course


7 hours (usually 1 day including breaks)


  • An understanding of digital marketing


  • Marketers


AI (Artificial Intelligence) is intelligence for machines to accomplish specific tasks by recognizing patterns in data. AI enables users to growth hack the success of digital marketing campaigns.

This instructor-led, live training (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.

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.

Course Outline


AI in Digital Marketing

  • What is AIDM?
  • The application of AIDM

Content Curation and Creation

  • Streamlining content with AI tools
  • Working with Curata, BuzzSumo, Crayon, and Scoop-It

Google Cloud AI

  • Creating and scaling chatbots
  • Integrating chatbots on a web application

SEO Optimization

  • Working with Market Brew

Email Task Automation

  • Automating email tasks with Siftrock

Tracking and Reporting

  • Tracking and reporting user behavior with BlueShift
  • Tracking and reporting data from social media platforms with Zoomph

Summary and Conclusion

Introduction to Data Science and AI using Python Training Course


35 hours (usually 5 days including breaks)




This is a 5 day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python 

Course Outline

Introduction to Data Science/AI

  • Knowledge acquisition through data
  • Knowledge representation
  • Value creation
  • Data Science overview
  • AI ecosystem and new approach to analytics
  • Key technologies

Data Science workflow

  • Crisp-dm
  • Data preparation
  • Model planning
  • Model building
  • Communication
  • Deployment

Data Science technologies

  • Languages used for prototyping
  • Big Data technologies
  • End to end solutions to common problems
  • Introduction to Python language
  • Integrating Python with Spark

AI in Business

  • AI ecosystem
  • Ethics of AI
  • How to drive AI in business

Data sources

  • Types of data
  • SQL vs NoSQL
  • Data Storage
  • Data preparation

Data Analysis – Statistical approach

  • Probability
  • Statistics
  • Statistical modeling
  • Applications in business using Python

Machine learning in business

  • Supervised vs unsupervised
  • Forecasting problems
  • Classfication problems
  • Clustering problems
  • Anomaly detection
  • Recommendation engines
  • Association pattern mining
  • Solving ML problems with Python language

Deep learning

  • Problems where traditional ML algorithms fails
  • Solving complicated problems with Deep Learning
  • Introduction to Tensorflow

Natural Language processing

Data visualization

  • Visual reporting outcomes from modeling
  • Common pitfalls in visualization
  • Data visualization with Python

From Data to Decision – communication

  • Making impact: data driven story telling
  • Influence effectivnes
  • Managing Data Science projects

Vertex AI Training Course


7 hours (usually 1 day including breaks)


  • Knowledge of machine learning


  • Software engineers
  • Machine learning enthusiasts


Vertex AI is a Google Cloud environment for completing machine learning tasks from experimentation, to deployment, to managing and monitoring models. It is a scalable infrastructure that provides user management capabilities and security controls over machine learning projects.

This instructor-led, live training (online or onsite) is aimed at beginner to intermediate-level 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.

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.

Course Outline


Overview of Vertex AI

Understanding AI Concepts

Setting up the Vertex AI Environment

Regression and Classification Concepts in Vertex AI

NLP Concepts in Vertex AI

Setting up a Containerize Training Code

Running a Training Job on Vertex AI

Deploying a Model Endpoint


Summary and Next Steps

Introduction to AI for Business

Amplifying Human Ingenuity with Intelligent Technology


  • No tools are required, just basic knowledge and experience on business administration


Peter Maynard, Director Program Management at Microsoft, explores what AI really is, why and how it will transform every business in every industry. Peter also uncovers how Microsoft technology is at the forefront of this transformation and show some scenarios, both present and future with respect to how this is helping business embrace digital transformation.

The purpose of the course is to highlight how underlying Digital Transformation in a number of enterprises is simply an algorithm. This algorithm will determine the success of how that company will leverage its data in the future and if it will ultimately survive. Moving on from that as background, there will be then explored the types of steps that a company can take to win in the algorithm wars and things that they should be conscious of. In the course, there will be presented a range of examples of companies that are winning in Digital Transformation through AI.

Who this course is for:

  • Professionals who want to explore what AI really is, why and how it will transform every business in every industry.

Course content

1 section • 12 lectures • 47m total length

Python with AI

Python Level 0


  • This course is for absolute beginners! No programming experience is required. If you can surf online, you are good to go.


Python programming is fun and useful, but starting from zero can be intimidating.

This course is designed to remove the intimidation factor of Python programming. It is for elementary or secondary students who are curious about Python programming, or who are scared of programming. The course provides bite-size videos. Each video covers one topic in about 10 minutes. The coding demos will show every steps. You can easily follow along and start to write Python programs.

There is no jargons, just plain explanations!

If you’re an adult and want to have a taste of Python programming, this is for you as well. Who would mind a quick and easy start?

The students will

  • Start with the general concept of Python, such as what it is and what it can do.
  • Learn how to install required software for Python programming.
  • Learn basic Python statements, such as print, input, data conversion, and if statements.
  • Practice Python coding.
  • Take pop quizzes to check knowledge understanding.
  • Write chatbot and math quiz programs.

After the course, the students will be able to:

  • Understand the general programming concept and process.
  • Understand what domains Python can be used.
  • Get comfortable with Visual Studio Code (Integrated Development Environment).
  • Understand Python fundamentals.
  • Ready for more advanced Python programming.

Who this course is for:

  • This course is for absolute beginners! It’s for everyone who wants to learn Python or who is scared of programming.

Course content

1 section • 13 lectures • 1h 59m total length

Implement Anomaly Detection AI algorithms with CloudShore

Using Artificial Intelligence for smart alerts – Part 1


  • Basic software development skills


Smart Alert is an AI-based platform that uses machine learning algorithms to analyze data and identify potential security threats. The system then sends alerts to users in real-time, providing them with the information they need to take action.

The machine learning algorithms used by Smart Alert are constantly evolving, as they learn from new data sets and experiences. This allows the system to become more accurate over time, and provides users with a more reliable service.

CloudShore course

CloudShore course is an online course that uses artificial intelligence to help you improve your alertness. The course includes a series of videos that will help you learn how to use AI to be more alert and responsive to your environment.

This training is an introduction to the new tool in the CloudShore Digital Workers platform. The contents focus on topics of Artificial Intelligence, Machine learning and some basic concepts for developers, project managers and sales professionals.

After the training, a student will understand the working principles of the smart alert tool and will be able to make use of its benefits.

This training is part of the CloudShore certification.

CloudShore University is an academic initiative from CloudShore. Created by Dr. Fernando Beker, is a comprehensive platform integrated with established universities to include courses about CloudShore in the university curricula.

Courses that start with code MRPA are valid credits for the IPIA Institute for its master’s degree in RPA.

Courses could take individually or in combination to get a CloudShore Certification.

Who this course is for:

  • People wishing to implement AI in their automation projects

Course content

5 sections • 5 lectures • 1h 53m total length