TinyML with Arduino Nano RP2040 Connect
Learn how to develop machine learning models for tiny microcontrollers with this comprehensive course. Understand the hardware requirements, explore the tinyML development framework, and create projects based on hand gestures and audio keyword detection. Discover the wide application domain of tinyML, from anomaly detection in machinery to healthcare. This course is perfect for beginners interested in developing low-cost, low-power microcontroller models. Join now and unlock the potential of tiny machine learning!
What you’ll learn
- To be able to understand hardware requirement for development of machine learning model for tiny MCUs
- Understanding the tinyML development framework
- To be able to create tinyML projects based upon hand gesture
- To be able to develop tinyML model with audio keyword detection
**Note: This course is not finalized yet. As you know, the TinyML field is constantly growing and developing. So, keeping in mind more sections with theoretical explanations with hands-on project ideas will be included in the near future.
Tiny machine learning, which targets battery-operated devices, is broadly defined as a rapidly expanding field of machine learning technologies and applications that includes hardware (dedicated integrated circuits), algorithms, and software that can perform on-device sensor data analytics at extremely low power, typically in the mW range and below. It eliminates the requirement to send data to the cloud for classification thus providing more security. Also, power-hungry processors are being replaced by a tiny MCU. Of course, there are limitations. The limitations came from limited hardware resources, clock speed, etc. Still, there are several application areas where high computation is not required and a machine learning-based solution is desirable. In that case, TinyML will come into the picture. It can be used to detect anomalies in machinery in a factory, it can predict maintenance requirements of the instruments, healthcare field, and so on. The application domain of TinyML is wide and the future is bright.
The primary objective of this course is to be familiar with TinyML development starting from data collection, model training, testing, and deployment. A low-cost Arduino nano RP2040 connect board having 265KB RAM and 16MB flash with in built accelerometer, Gyroscope, Microphone, temperature sensor, and wireless connectivity module (WiFi+Bluetooth) is used in this course and all example demonstrated here is tested on this board.
Who this course is for:
- Beginner, interested to develop machine learning model in low cost, low power microcontroller
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