Completion

CERTIFICATE

Introduction to Embedded Machine Learning

Name: Introduction to Embedded Machine Learning
Price: 2699 INR
Rating: 4.8 (662 reviews)

Learn to deploy machine learning models on microcontrollers and create smart embedded systems with hands-on projects using Arduino.

Course Cost

₹ 2,699

Intermediate

Skill Level

16 Hours

Self-paced lessons

This comprehensive course introduces embedded machine learning, focusing on deploying ML models to microcontrollers. You'll learn how machine learning works, train neural networks, and implement TinyML solutions. The course covers fundamental ML concepts, hardware considerations, and practical applications in motion and audio classification. Through hands-on projects with Arduino and Edge Impulse, you'll gain experience in data collection, feature extraction, model training, and deployment. No prior ML knowledge is required, though basic math skills and familiarity with embedded systems are recommended.

4.8

45,729 Enrolled

Alexander Fred-Ojala

Shawn Hymel

English

4.8

45,729 Enrolled

English

Alexander Fred-Ojala

Shawn Hymel

What you'll learn

Learn the basics of machine learning systems and their limitations

Master deploying ML models to microcontrollers

Understand how to use ML for embedded system decisions

Gain practical experience with neural networks and training

Explore audio classification and keyword spotting

Develop skills in feature extraction and model evaluation

Skills you'll gain

embedded machine learning

TinyML

Arduino

neural networks

microcontrollers

Edge Impulse

motion classification

audio classification

CNN

feature extraction

This course includes:

258 Minutes PreRecorded video

14 assignments

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

There are 3 modules in this course

This course provides a comprehensive introduction to embedded machine learning, focusing on deploying ML models to microcontrollers. Through three modules, students learn the fundamentals of machine learning, neural networks, and their implementation on embedded systems. The curriculum covers practical applications including motion classification and audio processing, with hands-on projects using Arduino hardware and Edge Impulse platform. Students gain experience in data collection, feature extraction, model training, and deployment, preparing them for real-world embedded ML applications.

Introduction to Machine Learning

Module 1 · 5 Hours to complete

In this module, we will introduce the concept of machine learning, how it can be used to solve problems, and its limitations. We will also cover how machine learning on embedded systems, such as single board computers and microcontrollers, can be effectively used to solve problems and create new types of computer interfaces.

Skills you'll gain

Machine learning basics

Embedded systems implementation

Data collection techniques

Feature extraction methods

Edge computing fundamentals

This course includes:

13 Videos

5 Assignments

15 Reading Materials

Introduction to Neural Networks

Module 2 · 6 Hours to complete

In this module, we will look at how neural networks work, how to train them, and how to use them to perform inference in an embedded system. We will continue the previous demo of creating a motion classification system using motion data collected from a smartphone or Arduino board.

Skills you'll gain

Neural networks

Model training

Motion classification

Embedded inference

Anomaly detection

This course includes:

10 Videos

5 Assignments

10 Reading Materials

Audio classification and Keyword Spotting

Module 3 · 5 Hours to complete

In this module, we cover audio classification on embedded systems. Specifically, we will go over the basics of extracting mel-frequency cepstral coefficients (MFCCs) as features from recorded audio, training a convolutional neural network (CNN) and deploying that neural network to a microcontroller.

Skills you'll gain

Audio feature extraction

MFCC analysis

CNN implementation

Embedded deployment

Sensor fusion

This course includes:

9 Videos

4 Assignments

1 Plugins

7 Reading Materials

Fee Structure

Payment options

Financial Aid

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

4.8

261

Reviews

45,936

Students

Course

Alexander Fred-Ojala

Alexander Fred-Ojala is a distinguished technology leader who serves as the CEO and Co-founder of Predli, a global emerging technology advisory firm, while maintaining significant academic roles at UC Berkeley. As the AI and Blockchain Director of the Learn2Launch Program and former Research Director of the Data Lab at UC Berkeley's Sutardja Center for Entrepreneurship & Technology, he has shaped the education of hundreds of engineering students through data science and blockchain courses. His expertise was recognized with the prestigious Amazon Alexa Innovation Fellowship in 2018-2019, making him one of just ten faculty members selected globally. His entrepreneurial experience includes being part of founding teams for multiple successful ventures, including Wheely's (YC 2015), Auranest (Series A), and InnoQuant, where he served as Co-founder & COO

4.8

261

Reviews

59,306

Students

Courses

Shawn Hymel

Shawn Hymel is an accomplished technical educator and content developer who specializes in making complex electronics and programming concepts accessible to learners of all ages. As the founder of Skal Risa, LLC since 2017, he creates educational videos, blogs, and courses for various technology clients. His professional background includes engineering positions at SparkFun Electronics, where he later transitioned into video production and marketing advisory roles. Currently, he serves as an instructor at Edge Impulse, where he teaches courses on embedded machine learning and computer vision. His contributions to technical education include developing comprehensive course materials on Real-Time Operating Systems (RTOS) and other advanced electronics topics. Beyond his professional work, he maintains an active presence in the electronics community by conducting workshops, and balances his technical pursuits with recreational interests like swing dancing.

Intermediate

Skill Level

16 Hours

Self-paced lessons

Course Cost

₹ 2,699

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.

Introduction to Embedded Machine Learning

What you'll learn

Skills you'll gain

This course includes:

Get a Completion Certificate

Get a Completion Certificate

Top companies offer this course to their employees

There are 3 modules in this course

Introduction to Machine Learning

Skills you'll gain

This course includes:

Introduction to Neural Networks

Skills you'll gain

This course includes:

Audio classification and Keyword Spotting

Skills you'll gain

This course includes:

Fee Structure

Payment options

Financial Aid

Reviews

Faculties

Frequently asked Questions

Do I need to buy hardware to take this course?

What prior knowledge do I need?

When will I have access to the lectures and assignments?

What will I get if I purchase the Certificate?

What is the refund policy?

Is financial aid available?