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Vijay Janapa Reddi
and
Laurence Moroney
What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.
Read more
What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.
Read more
What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.
TinyML is at the intersection of embedded Machine Learning (ML) applications, algorithms, hardware, and software. TinyML differs from mainstream machine learning (e.g., server and cloud) in that it requires not only software expertise, but also embedded-hardware expertise.
The first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.
Following Fundamentals of TinyML, the other courses in the TinyML Professional Certificate program will allow you to see the code behind widely-used Tiny ML applications—such as tiny devices and smartphones—and deploy code to your own physical TinyML device. Fundamentals of TinyML provides an introduction to TinyML and is not a prerequisite for Applications of TinyML or Deploying TinyML for those with sufficient machine learning and embedded systems experience.
Register for this course and see more details by visiting:
OpenCourser.com/course/osjowl/fundamentals
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What's inside
Learning objectives
- Fundamentals of machine learning (ml)
- Fundamentals of deep learning
- How to gather data for ml
- How to train and deploy ml models
- Understanding embedded ml
- Responsible ai design
- Fundamentals of machine learning (ml)
- Fundamentals of deep learning
- How to gather data for ml
- How to train and deploy ml models
- Understanding embedded ml
- Responsible ai design
Syllabus
Chapter 1: Welcome to TinyML
Chapter 1.1: Course Overview
Chapter 1.2: The Future of ML is Tiny and Bright
Chapter 1.3: TinyML Challenges
Read more
Syllabus
Chapter 1: Welcome to TinyML
Chapter 1.1: Course Overview
Chapter 1.2: The Future of ML is Tiny and Bright
Chapter 1.3: TinyML Challenges
Read more
Chapter 1.4: Getting Started
Chapter 2: Introduction to (Tiny) ML
Chapter 2.1: The Machine Learning Paradigm
Chapter 2.2: The Building Blocks of Deep Learning
Chapter 2.3: Exploring Machine Learning Scenarios
Chapter 2.4: Building a Computer Vision Model
Chapter 2.5: Responsible AI Design
Chapter 2.6: Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
For students curious about the intersection of ML, deep learning, embedded devices/systems
Covers fundamentals of both ML and DL
Taught by two instructors who hold esteemed positions in the field, ensuring high-quality instruction
Provides a strong foundation required for future courses in the TinyML Professional Certificate program
Explores industry-relevant applications of TinyML
Covers responsible AI design principles to promote ethical use of AI applications
Save this course
Save Fundamentals of TinyML to your list so you can find it easily later:
Save
Save
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 Fundamentals of TinyML with these
activities:
Refresh ML Fundamentals
Show steps
Review the fundamentals of ML to bridge any gaps in your knowledge and ensure a smoother learning experience in TinyML.
Browse courses on
Machine Learning
Show steps
-
Review key ML concepts such as data preprocessing, model training, and evaluation.
-
Go over foundational algorithms like linear regression, logistic regression, and decision trees.
-
Practice implementing simple ML models using a programming language like Python.
Read 'TinyML: Machine Learning with Tiny Devices and Edge Computing'
Show steps
Gain a comprehensive understanding of TinyML concepts and applications by exploring this authoritative text.
View
TinyML: Machine Learning with TensorFlow Lite...
on Amazon
Show steps
-
Read through the book's chapters, focusing on the fundamentals of TinyML.
-
Work through the examples and exercises provided in the book.
-
Summarize the key concepts and techniques discussed in the book.
Join a TinyML Study Group
Show steps
Collaborate with peers to enhance your understanding and problem-solving skills.
Browse courses on
TinyML
Show steps
-
Find or create a study group with other students enrolled in the TinyML course.
-
Set regular meeting times to discuss course material, work on assignments together.
-
Share knowledge, ask questions, and provide feedback to fellow group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve ML Coding Challenges
Show steps
Engage in practical exercises to solidify your understanding of ML concepts and algorithms.
Browse courses on
Machine Learning
Show steps
-
Find online ML coding challenges or use platforms like LeetCode or Kaggle.
-
Attempt to solve challenges involving data preprocessing, model training, and evaluation.
-
Analyze your solutions, identify areas for improvement, and learn from your mistakes.
-
Share your solutions and insights with peers or mentors for feedback.
Connect with TinyML Experts
Show steps
Seek guidance and support from experienced professionals in the field.
Browse courses on
TinyML
Show steps
-
Attend industry events or join online communities related to TinyML.
-
Reach out to researchers, engineers, or practitioners in the field.
-
Request mentorship or guidance on specific TinyML topics or projects.
Follow Online TinyML Tutorials
Show steps
Supplement your learning with guided tutorials to reinforce concepts and gain practical insights.
Browse courses on
TinyML
Show steps
-
Search for online tutorials and courses on TinyML offered by platforms like Coursera, Udemy, or edX.
-
Select tutorials that align with your learning goals and skill level.
-
Follow the tutorials step-by-step, completing exercises and assignments.
-
Use the tutorials as a reference resource for future projects or assignments.
Build a TinyML Project
Show steps
Apply your TinyML knowledge to a practical project, reinforcing your understanding and developing your skills.
Browse courses on
TinyML
Show steps
-
Identify a problem or need that can be addressed using TinyML.
-
Design and develop a TinyML solution using appropriate hardware and software.
-
Train and deploy your TinyML model on the target device.
-
Evaluate the performance of your TinyML solution and make improvements as needed.
Contribute to TinyML Open-Source Projects
Show steps
Engage with the TinyML community and contribute to real-world projects.
Browse courses on
TinyML
Show steps
-
Explore open-source TinyML projects on platforms like GitHub or GitLab.
-
Identify projects that align with your interests and skill level.
-
Contribute to projects by submitting bug reports, suggesting features, or writing code.
-
Collaborate with other developers and learn from their experiences.
Refresh ML Fundamentals
Show steps
Review the fundamentals of ML to bridge any gaps in your knowledge and ensure a smoother learning experience in TinyML.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review key ML concepts such as data preprocessing, model training, and evaluation.
- Go over foundational algorithms like linear regression, logistic regression, and decision trees.
- Practice implementing simple ML models using a programming language like Python.
Read 'TinyML: Machine Learning with Tiny Devices and Edge Computing'
Show steps
Gain a comprehensive understanding of TinyML concepts and applications by exploring this authoritative text.
View
TinyML: Machine Learning with TensorFlow Lite...
on Amazon
Show steps
Show steps
Show steps
- Read through the book's chapters, focusing on the fundamentals of TinyML.
- Work through the examples and exercises provided in the book.
- Summarize the key concepts and techniques discussed in the book.
Join a TinyML Study Group
Show steps
Collaborate with peers to enhance your understanding and problem-solving skills.
Browse courses on
TinyML
Show steps
Show steps
Show steps
- Find or create a study group with other students enrolled in the TinyML course.
- Set regular meeting times to discuss course material, work on assignments together.
- Share knowledge, ask questions, and provide feedback to fellow group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve ML Coding Challenges
Show steps
Engage in practical exercises to solidify your understanding of ML concepts and algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find online ML coding challenges or use platforms like LeetCode or Kaggle.
- Attempt to solve challenges involving data preprocessing, model training, and evaluation.
- Analyze your solutions, identify areas for improvement, and learn from your mistakes.
- Share your solutions and insights with peers or mentors for feedback.
Connect with TinyML Experts
Show steps
Seek guidance and support from experienced professionals in the field.
Browse courses on
TinyML
Show steps
Show steps
Show steps
- Attend industry events or join online communities related to TinyML.
- Reach out to researchers, engineers, or practitioners in the field.
- Request mentorship or guidance on specific TinyML topics or projects.
Follow Online TinyML Tutorials
Show steps
Supplement your learning with guided tutorials to reinforce concepts and gain practical insights.
Browse courses on
TinyML
Show steps
Show steps
Show steps
- Search for online tutorials and courses on TinyML offered by platforms like Coursera, Udemy, or edX.
- Select tutorials that align with your learning goals and skill level.
- Follow the tutorials step-by-step, completing exercises and assignments.
- Use the tutorials as a reference resource for future projects or assignments.
Build a TinyML Project
Show steps
Apply your TinyML knowledge to a practical project, reinforcing your understanding and developing your skills.
Browse courses on
TinyML
Show steps
Show steps
Show steps
- Identify a problem or need that can be addressed using TinyML.
- Design and develop a TinyML solution using appropriate hardware and software.
- Train and deploy your TinyML model on the target device.
- Evaluate the performance of your TinyML solution and make improvements as needed.
Contribute to TinyML Open-Source Projects
Show steps
Engage with the TinyML community and contribute to real-world projects.
Browse courses on
TinyML
Show steps
Show steps
Show steps
- Explore open-source TinyML projects on platforms like GitHub or GitLab.
- Identify projects that align with your interests and skill level.
- Contribute to projects by submitting bug reports, suggesting features, or writing code.
- Collaborate with other developers and learn from their experiences.
Career center
Learners who complete Fundamentals of TinyML will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer specializes in the engineering aspects of machine learning, and combines knowledge of software programming with a deep understanding of machine learning models and algorithms. They work on developing, deploying, and maintaining machine learning systems. Knowledge of model deployment, training, and data gathering, as taught in Fundamentals of TinyML, can help one succeed in this role.
Software Engineer
Software Engineer
Software Engineers apply engineering principles to the design, development, deployment, and maintenance of software systems. They can work on projects that range from web to mobile to enterprise applications. Knowledge of embedded ML, as taught in Fundamentals of TinyML, can help one succeed in this role.
Data Scientist
Data Scientist
A Data Scientist combines knowledge of mathematics, statistics, and computer science to tackle business problems. They work on everything from data cleaning and data analysis to the creation of machine learning models, and are often tasked with explaining complex technical concepts to non-technical stakeholders. Knowledge of model training, data gathering, and responsible AI design, as taught in Fundamentals of TinyML, can help one succeed in this role.
Embedded Systems Engineer
Embedded Systems Engineer
An Embedded Systems Engineer designs, develops, and tests embedded systems, which are computer systems that are designed to be part of a larger system. These systems are often used in industrial, medical, and automotive applications. Knowledge of embedded ML, as taught in Fundamentals of TinyML, can help one succeed in this role.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers are responsible for designing, developing, and deploying deep learning models. They work on a wide range of projects, from image recognition to natural language processing. The deep learning fundamentals taught in Fundamentals of TinyML can help someone succeed in this career.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They work on everything from data cleaning and data analysis to the creation of visualizations. Knowledge of data gathering, responsible AI design, and model deployment, as taught in Fundamentals of TinyML, can help someone succeed in this role.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and deploys AI systems. They work on a wide range of projects, from self-driving cars to medical diagnosis systems. Fundamentals of TinyML provides an introduction to the fundamentals of machine learning and deep learning, which are essential for success in this field.
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher develops new machine learning algorithms and techniques. They work on a wide range of problems, from fraud detection to medical diagnosis. Knowledge of model training, data gathering, and responsible AI design, as taught in Fundamentals of TinyML, can help someone succeed in this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. They work on a wide range of projects, from supply chain management to healthcare operations. Knowledge of data gathering, model training, and responsible AI design, as taught in Fundamentals of TinyML, can help someone succeed in this role.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work on a wide range of projects, from clinical trials to market research. Knowledge of data gathering, responsible AI design, and model training, as taught in Fundamentals of TinyML, can help one succeed in this role.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work on a wide range of projects, from data integration to data warehousing. Knowledge of data gathering, model deployment, and responsible AI design, as taught in Fundamentals of TinyML, can help one succeed in this role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They work on a wide range of projects, from risk management to portfolio optimization. Knowledge of data gathering, model training, and responsible AI design, as taught in Fundamentals of TinyML, can help someone succeed in this role.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to make better business decisions. They work on everything from market research to financial analysis. Knowledge of data gathering, responsible AI design, and model deployment, as taught in Fundamentals of TinyML, can help one succeed in this role.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and test robots. They work on a wide range of projects, from industrial robots to medical robots. Knowledge of embedded ML, as taught in Fundamentals of TinyML, can help one succeed in this role.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work on everything from market research to product design. Knowledge of responsible AI design, as taught in Fundamentals of TinyML, can help one succeed in this role.
For more career information including salaries, visit:
OpenCourser.com/course/osjowl/fundamentals
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fundamentals of TinyML.
Provides a comprehensive introduction to TinyML, covering the basics of machine learning, deep learning, and embedded devices and systems. It is an excellent resource for those who want to learn more about the fundamentals of TinyML and its applications.
Provides a comprehensive introduction to deep learning, covering the basics of deep neural networks, convolutional neural networks, and recurrent neural networks. It is an excellent resource for those who want to learn more about the theory and practice of deep learning.
Save
Provides a comprehensive introduction to embedded systems, covering the basics of hardware, software, and design. It valuable resource for those who want to learn more about the design and implementation of embedded systems.
Provides a practical introduction to machine learning, covering the basics of data preprocessing, model selection, and model evaluation. It valuable resource for those who want to learn more about the practical aspects of machine learning.
Save
Provides a comprehensive introduction to data-intensive applications, covering the basics of data modeling, data storage, and data processing. It valuable resource for those who want to learn more about the design and implementation of data-intensive applications.
Provides a comprehensive introduction to data science, covering the basics of data collection, data analysis, and data visualization. It valuable resource for those who want to learn more about the practical aspects of data science.
Provides a comprehensive introduction to machine learning for data scientists, covering the basics of supervised learning, unsupervised learning, and deep learning. It valuable resource for those who want to learn more about the theory and practice of machine learning.
Provides a comprehensive introduction to deep learning for natural language processing, covering the basics of natural language processing, deep neural networks, and deep learning models for natural language processing. It valuable resource for those who want to learn more about the theory and practice of deep learning for natural language processing.
Save
Provides a comprehensive introduction to computer vision, covering the basics of image processing, feature extraction, and object recognition. It valuable resource for those who want to learn more about the theory and practice of computer vision.
Provides a comprehensive introduction to speech and language processing, covering the basics of speech recognition, natural language processing, and speech synthesis. It valuable resource for those who want to learn more about the theory and practice of speech and language processing.
Provides a comprehensive introduction to reinforcement learning, covering the basics of reinforcement learning, Markov decision processes, and Q-learning. It valuable resource for those who want to learn more about the theory and practice of reinforcement learning.
Save
Provides a comprehensive introduction to generative adversarial networks, covering the basics of generative adversarial networks, deep learning, and machine learning. It valuable resource for those who want to learn more about the theory and practice of generative adversarial networks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/osjowl/fundamentals
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Effort
2 to 4 hours per week for 5 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructors
Vijay Janapa Reddi
Laurence Moroney
Session
April 24, 2024
- April 23, 2025
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
This course belongs to a
collection
called
Tiny Machine Learning (TinyML). It's comprised of three courses:
- Applications of TinyML
- Deploying TinyML
- Fundamentals of TinyML (viewing)
Good to know
For students curious about the intersection of ML, deep learning, embedded devices/systems
Covers fundamentals of both ML and DL
Taught by two instructors who hold esteemed positions in the field, ensuring high-quality instruction
Provides a strong foundation required for future courses in the TinyML Professional Certificate program
Explores industry-relevant applications of TinyML
Covers responsible AI design principles to promote ethical use of AI applications
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