Online or onsite, instructor-led live microcontroller training courses demonstrate through hands-on practice how to set up and program microcontrollers for use in controlling real-world devices such as lights, motors and motion detection sensors. Microcontrollers can also be used as complete server systems for dedicated tasks such as printing and web serving.
Microcontroller training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Microcontroller training can be carried out locally on customer premises in Rome or in NobleProg corporate training centers in Rome.
NobleProg -- Your Local Training Provider
Rome
NobleProg Rome, Via Clelia Bertini Attilj 4, Rome, italy, 34D
CoworkTalenti is located in the north part of Rome, in the discrictTalenti-Montesacro, a few minutes from the highway ring. If you go on via Nomentana, you can reach easily the location.
This instructor-led, live training in Rome (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its benefits for edge AI applications.
Set up a development environment for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
Optimize AI models for power efficiency and memory constraints.
This instructor-led, live training in Rome (online or onsite) is aimed at engineers who wish to learn how to use embedded C to program various types of microcontrollers based on different processor architectures (8051, ARM CORTEX M-3, and ARM9).
In this instructor-led, live training in Rome, participants will learn how to program the Arduino for real-world usage, such as to control lights, motors and motion detection sensors. This course assumes the use of real hardware components in a live lab environment (not software-simulated hardware).
By the end of this training, participants will be able to:
Program Arduino to control lights, motors, and other devices.
Understand Arduino's architecture, including inputs and connectors for add-on devices.
Add third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to extend Arduino's functionality.
Understand the various options in programming languages, from C to drag-and-drop languages.
Test, debug, and deploy the Arduino to solve real world problems.
Raspberry Pi is a very small, single-board computer.
In this instructor-led, live training, participants will learn how to set up and program the Raspberry Pi to serve as an interactive and powerful embedded system.
By the end of this training, participants will be able to:
Set up an IDE (integrated development environment) for maximum development productivity
Program Raspberry Pi to control devices such as motion sensor, alarms, web servers and printers.
Understand Raspberry Pi's architecture, including inputs and connectors for add-on devices.
Understand the various options in programming languages and operating systems
Test, debug, and deploy the Raspberry Pi to solve real world problems
Audience
Developers
Hardware/software technicians
Technical persons in all industries
Hobbyists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
Raspberry Pi supports various operating systems and programming languages. This course will use Linux-based Raspbian as the operating system and Python as the programming language. To request a specific setup, please contact us to arrange.
Participants are responsible for purchasing the Raspberry Pi hardware and components.
This instructor-led, live training in Rome (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.
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Testimonials (2)
Sean was a dynamic speaker and the hands-on exercises were very interesting and I can see how they will be really applicable.
Temira Koenig - Yeshiva University
Course - Raspberry Pi for Beginners
The aquisition of useful knowlwdge and clarification of some things I was not sure of peviously.
Kenneth Mahoney - University of Glasgow
Course - Arduino: Programming a Microcontroller for Beginners
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