The 10 best Embedded Systems & IoT courses in 2026

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

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the exciting world of IoT with SwiftIO Playground. In this hands-on course, you'll embark on 46 unique playground projects, ranging from basic LED control to complex applications such as weather stations, accelerometer-based locks, and even Tic-Tac-Toe games. Each project helps you build real-world IoT systems using Swift code and hardware, empowering you to dev

This course on integrating sensors with your Raspberry Pi is course 3 of a Coursera Specialization and can be taken separately or as part of the specialization. Although some material and explanations from the prior two courses are used, this course largely assumes no prior experience with sensors or data processing other than ideas about your own projects and an interest in building projects with sensors. This course focuses on core concepts and techniques in designing and integrating any sensor, rather than overly specific examples to copy. This method allows you to use these concepts in y

With Amazon Kinesis Video Streams, you can build media streaming applications for Internet of Things (IoT) video devices and real-time computer vision machine learning (ML) applications. In this course, you will learn the benefits and technical concepts of Kinesis Video Streams. If you are new to the service, you will learn how to start using Kinesis Video Streams through a demonstration using the AWS Management Console. You will learn about the native architecture and how the built-in features can help you simplify image extraction through APIs or automated image extraction from metadata tag

This hands-on course teaches working professionals and engineering students how to design, build, and deploy intelligent analytics dashboards without writing code. Using AI-native no-code platforms (Glide, Softr, Bubble, Retool, Zapier Tables, and ChatGPT/Claude-driven data tools), learners will build a single end-to-end capstone project: "GreenPulse Analytics" a Smart ESG (Environmental, Social, Governance) & Energy Consumption Dashboard for mid-sized manufacturing plants. Instead of the common sales/marketing dashboard, this uncommon but high-demand use case covers IoT-style sensor data, ano

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you’ll dive into the world of Raspberry Pi and IoT, learning to leverage Node-RED for building and deploying IoT solutions. You’ll gain hands-on experience in interfacing sensors, controlling relays, and creating interactive flows that integrate hardware and software seamlessly. By understanding Raspberry Pi fundamentals and learning to wor

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. This Specialization builds u

In this course, learners will be introduced to the fundamental concepts of computer-aided manufacturing and its implementation through open-source software. The course involves topics related to Computer-Aided Manufacturing (CAM), Computer-Aided Process Planning (CAPP), Essentials of CNC machines and Robotic Arms, NC programming, and Toolpath generation through open-source software and CAD/CAM tools PowerShape and PowerMill. This course is best suited for undergraduate students in mechanical engineering. Professionals working across IoT, Product Design, Materials, Mechanics, and System Design

A modern VLSI chip has a zillion parts -- logic, control, memory, interconnect, etc. How do we design these complex chips? Answer: CAD software tools. Learn how to build thesA modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of t

Modern embedded systems demand sophisticated build architectures and platform integration strategies. This Short Course was created to help Computer Systems Engineering and Architecture professionals master advanced embedded toolchains and concurrency optimization. By completing this course, you'll configure production-ready cross-compiled builds, generate custom Linux images, and implement lock-free concurrency patterns that eliminate priority inversion in real-time systems. By the end of this course, you will be able to: • Apply CMake or Bazel toolchains to configure a cross-compiled build