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Introduction to Embedded Machine Learning
  • Level Professional
  • المدة 17 ساعات hours
  • الطبع بواسطة Edge Impulse
  • Offered by

عن

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course. Familiarity with Arduino and microcontrollers is advised to understand some topics as well as to tackle the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. We will cover the concepts and vocabulary necessary to understand the fundamentals of machine learning as well as provide demonstrations and projects to give you hands-on experience.

الوحدات

Introduction to the Course

    • 2 Videos
    • 5 Readings
    • 1 Discussion
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1 Discussions

  • Meet and Greet

2 Videos

  • Welcome to the Course
  • Instructor Introductions

5 Readings

  • Syllabus
  • Required Hardware
  • Errata and Changes
  • Getting Help
  • Slides

Introduction to Machine Learning

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Machine Learning and Limitations

2 Videos

  • What is Machine Learning?
  • Limitations and Ethics of Machine Learning

2 Readings

  • Limitations of Machine Learning
  • Slides

Embedded Machine Learning

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Embedded Machine Learning

3 Videos

  • Machine Learning on Embedded Devices
  • Machine Learning Specific Hardware
  • Machine Learning Software Frameworks

2 Readings

  • Machine Learning on Microcontrollers
  • Slides

Using Edge Impulse to Collect Data

    • 2 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Data Collection

2 Videos

  • Getting Started with Edge Impulse
  • Data Collection

3 Readings

  • Edge Impulse CLI Installation Troubleshooting
  • What Makes a Good Dataset
  • Slides

Feature Extraction

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Feature Extraction

3 Videos

  • Feature Extraction from Motion Data
  • Feature Selection in Edge Impulse
  • Machine Learning Pipeline

2 Readings

  • Feature Selection and Extraction
  • Slides

Review

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Machine Learning Overview

1 Discussions

  • Machine Learning in Your Life

1 Videos

  • Review of Module 1

1 Readings

  • Slides

Neural Networks and Training

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Neural Networks and Training

2 Videos

  • Introduction to Neural Networks
  • Model Training in Edge Impulse

2 Readings

  • Neural Networks and Training
  • Slides

Model Evaluation

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Evaluation, Underfitting, and Overfitting

2 Videos

  • How to Evaluate a Model
  • Underfitting and Overfitting

2 Readings

  • Evaluation, Underfitting, and Overfitting
  • Slides

Deploying a Model

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Deploy Model to Embedded System

3 Videos

  • How to Use a Model for Inference
  • Testing Inference with a Smartphone
  • How to Deploy a Trained Model to Arduino

2 Readings

  • Using a Model for Inference
  • Slides

Anomaly Detection

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Anomaly Detection

2 Videos

  • Anomaly Detection
  • Industrial Embedded Machine Learning Demo

2 Readings

  • Anomaly Detection
  • Slides

Project and Review

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Motion Classification and Anomaly Detection

1 Discussions

  • Share Your Motion Detection Project!

1 Videos

  • Module Review

2 Readings

  • Project - Motion Detection
  • Slides

Sampling Rate and Bit Depth

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Audio Classification and Sampling Audio Signals

2 Videos

  • Introduction to Audio Classification
  • Audio Data Capture

2 Readings

  • Sample Rate and Bit Depth
  • Slides

Audio Features and Convolutional Neural Networks

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • MFCCs and CNNs

3 Videos

  • Audio Feature Extraction
  • Introduction to Convolutional Neural Networks
  • Modifying the Neural Network

2 Readings

  • MFCCs and CNNs
  • Slides

Deployment to Embedded Systems

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Implementation Strategies

3 Videos

  • Deploy Keyword Spotting System
  • Implementation Strategies
  • Sensor Fusion

2 Readings

  • Implementation Strategies and Sensor Fusion
  • Slides

Project and Review

    • 1 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Plugin
    • 1 Assignment
Show info about module content

1 Assignment

  • Audio Classification

1 Discussions

  • Share Your Audio Classification Project!

1 Videos

  • Conclusion

1 Readings

  • Project - Sound Classification

Auto Summary

Dive into the world of embedded machine learning with this professional course by Coursera. Designed for Data Science and AI enthusiasts, it offers a comprehensive overview of training and deploying neural networks on microcontrollers, also known as TinyML. No prior ML knowledge is required, but a basic understanding of Arduino and microcontrollers is recommended. The course includes hands-on projects and quizzes, spanning over 1020 minutes. Subscription options include Starter and Professional, making it accessible for various learning needs. Perfect for anyone looking to blend machine learning with embedded systems.

Shawn Hymel
Instructors

Shawn Hymel

Alexander Fred-Ojala
Instructors

Alexander Fred-Ojala