TinyML with Arduino Nano RP2040 Connect

Machine learning model development for tiny low power microcontroller such as Arduino nano RP2040 connect.

4.70 (13 reviews)
Udemy
platform
English
language
Hardware
category
instructor
TinyML with Arduino Nano RP2040 Connect
75
students
2.5 hours
content
Jul 2023
last update
$39.99
regular price

What you will 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

To be able to create own classification model using Decision Tree classifier from Scikit-learn

Why take this course?

**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.

Screenshots

TinyML with Arduino Nano RP2040 Connect - Screenshot_01TinyML with Arduino Nano RP2040 Connect - Screenshot_02TinyML with Arduino Nano RP2040 Connect - Screenshot_03TinyML with Arduino Nano RP2040 Connect - Screenshot_04

Reviews

Brian
September 29, 2023
The Material was generally well prepared and presented, and *importantly* I would do it again knowing what I know now. The instructor always replied in a very timely manner. But as nothing is perfect I wanted to give a critic that I hope the instructor will take to heart and as they are intended. (1) I'm and naturally born American and was previously married to a Bengali for 13 years and attended countless Indian functions with her, as well having a large percentage of my coworkers from India. I have a lot of experience getting my ear tuned to Indian accents. However, yours is very strong so I think you need to SLOW down your pace of speaking everywhere, there were a couple of sections where your mouse was whipping around the screen and you were talking very fast. I am not suggesting getting rid of the accent but I would suggest practicing pacing, breathing, and a slower cadence. In my experience the best courses are when the instructors PRACTICE talking slowly and succinctly even when their accent is very neutral - better bored than confused. It's okay to breath. I had to play these sections over and over, and still feel like I was missing the point - i.e. (2) in section 7 I could never understand the rational for why you picked only THOSE two data sets, I understand the data has to be different so it can tell a wave from a circle, but didn't understand the reasons for the exclusion of much of it (understood the gyro exclusion). Much of this I think was because of the speed you went through it. (3) The beginning of one lesson starts with Google Colab on the screen, had it not been for a previous course where the instructor went though what it was and how it works, I would have been completely snowed on where it came from, etc. A couple of minutes of explanation whenever you pull in new tools would be really helpful. A couple of minutes for any tool which might be new to some users would be advisable. (4) The code I had was different from your code, and it was precisely at the point where the code was different it failed. Changing it made no difference however. I've experienced this in other courses it's very frustrating as the last part of the course depended on the output of the failing code, so from there on out it was just watching the video and trying to understand - which isn't usually too hard but it's in the doing that it sticks (for me at least). Thank You for the Course, I look forward to future courses you do.
Ricky
July 28, 2023
Good lectures so far. I have been able to follow through on the projects. Little bit light on the explanation for the feature generation sections - but it may be too much to expect in-depth explanations for the different types of data and conversion to features. The best part I liked about the class was that I was getting stuck in several places and on posting the questions - the responses were prompt. The trainer tried to help as much as possible.
Lauryn
July 2, 2023
He is good and I'm learning what is needed for my first Arduino project using TinyML which is wake-word detection.
Pavel
November 23, 2022
Good starting point for people who have basic programming knowledge and want to try tiny ml projects. It would be nice to create a separate section in this course about impulse design - typical configurations with short explanation.

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4917864
udemy ID
10/7/2022
course created date
1/14/2023
course indexed date
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