Applications of TinyML from edX

Do you know what happens when you say “OK Google” to a Google device? Is your Google Home always listening?

Following on the Foundations of Tiny ML course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.

Dive into the code for using sensor data for tasks such as gesture detection and voice recognition. Focusing on the neural network of the applications, specifically on training and inference, you will review the code behind “OK Google,” “Alexa,” and smartphone features on Android and Apple . Learn about real-word industry applications of TinyML as well as Keyword Spotting, Visual Wake Words, Anomaly Detection, Dataset Engineering, and Responsible Artificial Intelligence.

Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The second course in the TinyML Professional Certificate program, Applications of TinyML shows you the code behind some of the world’s most widely-used TinyML devices.

What's inside

Learning objectives

The code behind some of the most widely used applications of tinyml
Real-word industry applications of tinyml
Principles of keyword spotting
Principles of visual wake words

Concept of anomaly detection
Principles of dataset engineering
Responsible ai development

The code behind some of the most widely used applications of tinyml
Real-word industry applications of tinyml
Principles of keyword spotting
Principles of visual wake words
Concept of anomaly detection
Principles of dataset engineering
Responsible ai development

Syllabus

Chapter 1.1: Welcome to Applications of TinyML

Chapter 1.2: AI Lifecycle and ML Workflow

Chapter 1.3: Machine Learning on Mobile and Edge IoT Devices - Part 1

Chapter 1.4: Machine Learning on Mobile and Edge IoT Devices - Part 2

Chapter 1.5: Keyword Spotting

Chapter 1.6: Data Engineering for TinyML Applications

Chapter 1.7: Visual Wake Words

Chapter 1.8: Anomaly Detection

Chapter 1.9: Responsible AI Development

Chapter 1.10: Summary

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Examines the code behind industry-leading TinyML devices

Introduces TinyML applications and their usage in real-world scenarios

Provides a deep understanding of the workflow involved in ML on mobile and edge devices

Teaches the principles of essential TinyML applications, e.g., keyword spotting, anomaly detection

Emphasizes the importance of responsible AI development, a crucial topic for ML practitioners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Applications of TinyML with these activities:

Review basic machine learning concepts

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Reviewing basic machine learning concepts will help refresh your knowledge and provide a solid foundation for TinyML.

Browse courses on Machine Learning

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Go through online tutorials or textbooks on machine learning basics
Review concepts such as supervised and unsupervised learning, feature engineering, and model evaluation

Seek mentorship from experts in TinyML

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Finding a mentor can provide you with guidance, support, and valuable insights from experienced professionals in the field.

Browse courses on TinyML

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Identify potential mentors with expertise in TinyML
Reach out to them and express your interest in mentorship
Establish regular meetings or communication channels

Participate in a study group or online forum for TinyML

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Engaging with peers in a study group or online forum will enhance your understanding through discussions and knowledge sharing.

Browse courses on TinyML

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Join a study group or online forum focused on TinyML
Participate in discussions and ask questions
Share your knowledge and help others

Five other activities

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Solve coding challenges related to TinyML

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Solve coding challenges related to TinyML to improve your understanding of the code behind practical applications.

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Find a coding challenge platform
Choose challenges related to TinyML
Solve the challenges and review solutions

Practice deploying machine learning models on embedded devices

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Practice deploying machine learning models on embedded devices to reinforce your understanding of the course concepts.

Browse courses on Keyword Spotting

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Set up a development environment for TinyML
Train a machine learning model for a specific task
Optimize the model for deployment on an embedded device
Deploy the model and evaluate its performance

Follow tutorials on advanced TinyML techniques

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Follow tutorials on advanced TinyML techniques to expand your knowledge and explore new applications.

Browse courses on TinyML

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Identify specific areas of TinyML you want to explore
Search for tutorials on these advanced topics
Follow the tutorials and complete the exercises

Create a blog post or article on a TinyML topic

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Creating a blog post or article on a TinyML topic will help you synthesize your knowledge and share it with the community.

Browse courses on TinyML

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Choose a topic related to TinyML
Research the topic and gather information
Write a well-structured and informative blog post or article
Publish your content on a platform like Medium or your own website

Contribute to an open-source TinyML project

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Contributing to an open-source TinyML project will give you hands-on experience and allow you to collaborate with others in the community.

Browse courses on TinyML

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Find an open-source TinyML project that aligns with your interests
Review the project documentation and codebase
Identify an issue or feature to work on
Submit a pull request with your contributions
Collaborate with project maintainers to improve your contributions

Career center

Learners who complete Applications of TinyML will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer deploys machine learning models to help businesses gain insights from their data. After learning about the principles of keyword spotting and anomaly detection in this course, you will be well-equipped to deploy machine learning models for tasks such as detecting fraud or identifying customer churn.

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Data Scientist

Data Scientists use their knowledge of machine learning and statistics to solve business problems. The principles of visual wake words and responsible AI development in this course will help you become a more effective Data Scientist by broadening your understanding of machine learning techniques and ethical considerations.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course will help you build a foundation in TinyML, which is a rapidly growing area of software development.

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Product Manager

Product Managers are responsible for the development and launch of new products. The principles of dataset engineering in this course will help you gain a better understanding of how to gather data and prepare it for use in machine learning models. This knowledge will be invaluable in your role as a Product Manager.

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Technical Writer

Technical Writers create documentation for software and other technical products. In this course, you will learn the code behind some of the most widely used applications of TinyML, which will help you to write clear and concise documentation for machine learning products.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations and to develop financial plans. The principles of data engineering in this course will help you gain a better understanding of how to gather and interpret data, which will be valuable in your role as a Financial Analyst.

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Risk Analyst

Risk Analysts identify and assess risks to businesses and develop strategies to mitigate those risks. The principles of responsible AI development in this course will help you gain a better understanding of the ethical considerations involved in using machine learning, which will be valuable in your role as a Risk Analyst.

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Consultant

Consultants help businesses to improve their operations and performance. The principles of responsible AI development in this course will help you gain a better understanding of the ethical considerations involved in using machine learning, which will be valuable in your role as a Consultant.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course will help you build a foundation in TinyML, which can be used to develop models for tasks such as predicting stock prices or identifying fraud.

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Auditor

Auditors examine financial records and other documents to ensure that businesses are complying with laws and regulations. The principles of data engineering in this course will help you gain a better understanding of how to gather and interpret data, which will be valuable in your role as an Auditor.

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Compliance Analyst

Compliance Analysts ensure that businesses comply with laws and regulations. The principles of responsible AI development in this course will help you gain a better understanding of the ethical considerations involved in using machine learning, which will be valuable in your role as a Compliance Analyst.

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Business Analyst

Business Analysts help businesses to improve their operations and performance. The principles of responsible AI development in this course will help you gain a better understanding of the ethical considerations involved in using machine learning, which will be valuable in your role as a Business Analyst.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve problems in a variety of industries. The principles of anomaly detection in this course will help you gain a better understanding of how to identify and respond to unusual events, which will be valuable in your role as an Operations Research Analyst.

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Statistician

Statisticians use statistical methods to collect, analyze, and interpret data. The principles of dataset engineering in this course will help you gain a better understanding of how to gather data and prepare it for use in statistical models.

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Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course will help you build a foundation in TinyML, which can be used to analyze data from sensors and other embedded devices.

See salaries and explore the career path for Data Analyst