Course Summary
Learn how to apply machine learning techniques in embedded systems, including microcontrollers and other low-power devices, in this course. Gain practical skills through hands-on projects and explore the unique challenges of working with constrained hardware.Key Learning Points
- Apply machine learning techniques to embedded systems
- Develop solutions for microcontrollers and other low-power devices
- Explore the unique challenges of working with constrained hardware
Related Topics for further study
Learning Outcomes
- Apply machine learning techniques to embedded systems
- Develop solutions for microcontrollers and other low-power devices
- Understand the unique challenges of working with constrained hardware
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge
- Familiarity with embedded systems and microcontrollers
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Hands-on projects
Similar Courses
- Advanced Embedded Systems
- Machine Learning for IoT
- Embedded Systems Programming
Related Education Paths
Notable People in This Field
- Founder of deeplearning.ai
- CEO of Tesla
- Co-Director of Stanford's Human-Centered AI Institute
Related Books
Description
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.
Knowledge
- The basics of a machine learning system
- How to deploy a machine learning model to a microcontroller
- How to use machine learning to make decisions and predictions in an embedded system
Outline
- Introduction to Machine Learning
- Welcome to the Course
- Instructor Introductions
- What is Machine Learning?
- Limitations and Ethics of Machine Learning
- Machine Learning on Embedded Devices
- Machine Learning Specific Hardware
- Machine Learning Software Frameworks
- Getting Started with Edge Impulse
- Data Collection
- Feature Extraction from Motion Data
- Feature Selection in Edge Impulse
- Machine Learning Pipeline
- Review of Module 1
- Syllabus
- Required Hardware
- Getting Help
- Slides
- Limitations of Machine Learning
- Machine Learning on Microcontrollers
- Slides
- Edge Impulse CLI Installation Troubleshooting
- What Makes a Good Dataset
- Slides
- Feature Selection and Extraction
- Slides
- Slides
- Machine Learning and Limitations
- Embedded Machine Learning
- Data Collection
- Feature Extraction
- Machine Learning Overview
- Introduction to Neural Networks
- Introduction to Neural Networks
- Model Training in Edge Impulse
- How to Evaluate a Model
- Underfitting and Overfitting
- How to Use a Model for Inference
- Testing Inference with a Smartphone
- How to Deploy a Trained Model to Arduino
- Anomaly Detection
- Industrial Embedded Machine Learning Demo
- Module Review
- Neural Networks and Training
- Slides
- Evaluation, Underfitting, and Overfitting
- Slides
- Using a Model for Inference
- Slides
- Anomaly Detection
- Slides
- Project - Motion Detection
- Slides
- Neural Networks and Training
- Evaluation, Underfitting, and Overfitting
- Deploy Model to Embedded System
- Anomaly Detection
- Motion Classification and Anomaly Detection
- Audio classification and Keyword Spotting
- Introduction to Audio Classification
- Audio Data Capture
- Audio Feature Extraction
- Introduction to Convolutional Neural Networks
- Modifying the Neural Network
- Deploy Keyword Spotting System
- Implementation Strategies
- Sensor Fusion
- Conclusion
- Sample Rate and Bit Depth
- Slides
- MFCCs and CNNs
- Slides
- Implementation Strategies and Sensor Fusion
- Slides
- Project - Sound Classification
- Audio Classification and Sampling Audio Signals
- MFCCs and CNNs
- Implementation Strategies
- Audio Classification
Summary of User Reviews
Discover how to implement machine learning algorithms in embedded systems with this comprehensive course. Students rave about its practicality and engaging content, making it a top choice for those looking to dive into this field.Key Aspect Users Liked About This Course
Many users praised the practical approach of the course, with real-world examples and hands-on exercises that helped them understand the material better.Pros from User Reviews
- Practical and engaging content that's easy to follow
- Real-world examples and hands-on exercises
- Great introduction to embedded machine learning
- Highly knowledgeable and supportive instructors
- Good balance of theory and practice
Cons from User Reviews
- Some users found the course too basic
- Not enough emphasis on more advanced topics
- Lack of interaction with other students
- Limited community support
- Not suitable for those with no prior programming experience
Shawn Hymel
4.8 Raiting
Zero To Hero: Apache Maven
- 3.7
-
![]()
![]()
![]()
![]()
![]()
Become a Maven Expert: Step-by-Step Introduction for Beginners Course Overview Zero To Hero series on Apache Maven(3. 3. Why Should You Learn Maven?...
Tutorial: Set up Multi Org Hyperledger Fabric v1.4 Network
- 0.0
-
![]()
![]()
![]()
![]()
![]()
A course that will help you get your Fabric Application Deployed in a multi-org Fabric Setup Locally | Cloud PLEASE DO NOT Subscribe to this course...
Introduction to Machine Learning
- 4.7
-
![]()
![]()
![]()
![]()
![]()
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc....
Artificial Intelligence on Microsoft Azure
- 4.7
-
![]()
![]()
![]()
![]()
![]()
In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI....
