Introduction to Embedded Machine Learning
5.0 /5- Offered byCoursera
Introduction to Embedded Machine Learning at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Embedded Machine Learning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
- Approx. 15 hours to complete
- English Subtitles: English
Introduction to Embedded Machine Learning at Coursera Course details
- 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.
Introduction to Embedded Machine Learning at Coursera Curriculum
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
Limitations of Machine Learning
Machine Learning on Microcontrollers
Edge Impulse CLI Installation Troubleshooting
What Makes a Good Dataset
Feature Selection and Extraction
Machine Learning and Limitations
Embedded Machine Learning
New Quiz
New Quiz
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
Evaluation, Underfitting, and Overfitting
Using a Model for Inference
Anomaly Detection
Project - Motion Detection
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
MFCCs and CNNs
Implementation Strategies and Sensor Fusion
Project - Sound Classification
Audio Classification and Sampling Audio Signals
MFCCs and CNNs
Implementation Strategies
Audio Classification
Introduction to Embedded Machine Learning at Coursera Admission Process
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