Intro to Embedded Machine Learning

Intro to Embedded Machine Learning, available at $49.99, has an average rating of 3.45, with 20 lectures, 6 quizzes, based on 244 reviews, and has 1856 subscribers.

You will learn about Embedded Systems Machine Learning TinyML Embedded Machine Learning IoT This course is ideal for individuals who are Beginner students curious about embedded machine learning It is particularly useful for Beginner students curious about embedded machine learning.

Enroll now: Intro to Embedded Machine Learning

Summary

Title: Intro to Embedded Machine Learning

Price: $49.99

Average Rating: 3.45

Number of Lectures: 20

Number of Quizzes: 6

Number of Published Lectures: 20

Number of Published Quizzes: 6

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Embedded Systems

Machine Learning

TinyML

Embedded Machine Learning

IoT

Who Should Attend

Beginner students curious about embedded machine learning

Target Audiences

Beginner students curious about embedded machine learning

In this course, you will learn more about the field of embedded machine learning. In recent years, technological advances in embedded systems have enabled microcontrollers to run complicated machine learning models. Embedded devices for machine learning applications can fulfill many tasks in the industry. One typical example: sensor devices that detect acoustic or optical anomalies and discrepancies and, in this way, support quality assurance in production or system condition monitoring. In addition to cameras for monitoring visual parameters and microphones for recording soundwaves, these devices also use sensors for, for instance, vibration, contact, voltage, current, speed, pressure, and temperature.

Even though there is plenty of educational content on embedded systems and machine learning individually, educational content on embedded ML has yet to catch up. This course attempts to fill that void by providing fundamentals of embedded systems, machine learning, and Tiny ML. This course will conclude with an interactive project where the learner will get to create their own specialized embedded ML project. This project will be based on acoustic event detection using a microcontroller or your own mobile device. By the end of the course, you will be able to pick your own classifications and audio and train and deploy a machine learning model yourself. This is a great way to introduce yourself to and gain valuable experience in the field of embedded machine learning.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Fundamentals of Embedded Systems

Lecture 1: Microcontrollers

Lecture 2: Debugging

Lecture 3: Threads

Chapter 3: Fundamentals of Machine Learning

Lecture 1: ML Basics

Lecture 2: Supervised Learning

Chapter 4: Fundamentals of TinyML

Lecture 1: Tiny ML

Lecture 2: Acoustic Event Detection Example

Chapter 5: Embedded ML Project

Lecture 1: Tutorial Introduction

Lecture 2: Edge Impulse

Lecture 3: Create Account

Lecture 4: Connect Device

Lecture 5: Connect Device – Phone View

Lecture 6: Collect Data – Phone View

Lecture 7: Observe Data

Lecture 8: Generate Machine Learning Model

Lecture 9: Retrain Model

Lecture 10: Test Model – Phone View

Lecture 11: Next Steps

Chapter 6: Conclusion

Lecture 1: How to become an Embedded ML Engineer

Instructors

Ashvin Roharia
Software Engineer at Silicon Labs

Rating Distribution

1 stars: 12 votes

2 stars: 12 votes

3 stars: 46 votes

4 stars: 70 votes

5 stars: 104 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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