MathWorks
Public University • US
Showing 28 courses from MathWorks
Powering the Future with Electrification
MathWorks (via Coursera)
As companies pursue new strategies for electrification, engineers skilled in converting traditional systems to efficient, grid-powered models are in high demand. Ideal for engineers, scientists, as well as anyone interested in electrified systems, this course offers a comprehensive overview of electrification and the critical role of modeling and simulation in designing effective electrified systems. Through engaging modules, you’ll explore the challenges of designing and simulating electrified systems across diverse applications and discover how MATLAB, Simulink, and Simscape can make modeling complex systems easier. Real-world case studies, such as electric vehicle simulations and integrating solar panels into the grid, provide practical insights into the applications of these technologies. Enroll to start your journey into electrification and understand the expertise required to succeed.
Machine Learning for Computer Vision
MathWorks (via Coursera)
In the second course of the Computer Vision for Engineering and Science specialization, you will perform two of the most common computer vision tasks: classifying images and detecting objects. You will apply the entire machine learning workflow, from preparing your data to evaluating your results. By the end of this course, you’ll train machine learning models to classify images of street signs and detect material defects. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this specialization, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.
Medical Image Processing
MathWorks (via Coursera)
Medical image processing is pivotal in diagnosing diseases, planning treatment, and monitoring patients. Recent advances are revolutionizing how healthcare professionals understand and interact with complex medical conditions. This course is designed to equip engineers, scientists, and healthcare professionals with the in-demand skills to work with raw medical image data, enabling advancements in patient care and contributing to groundbreaking research in medical diagnostics and treatment strategies. Throughout this course, you will learn how to import and analyze common medical image formats, accurately view 2D and 3D images, and adjust image orientation and contrast for better analysis. The course takes you deeper into aligning and labeling 3D images like MRIs, equipping you with essential skills for segmentation and deep learning applications. By the end, you'll be proficient in identifying critical regions within medical images and extracting those regions for further analysis. In this course, you will use MATLAB, the go-to choice for millions working in engineering and science. MATLAB is an intuitive, low-code environment. You will use the specialized Medical Imaging Toolbox to simplify importing and visualizing complicated medical data files so you can quickly accomplish your image processing tasks. To be successful in this course, you should have some prior exposure to MATLAB. To familiarize yourself with MATLAB, complete the free, two-hour MATLAB Onramp. If you want more in-depth image processing skills, you can also enroll in the Image Processing for Engineering and Science specialization. The skills learned in the specialization can be applied to medical images.
Image Segmentation, Filtering, and Region Analysis
MathWorks (via Coursera)
In this course, you will build on the skills learned in Introduction to Image Processing to work through common complications such as noise. You’ll use spatial filters to deal with different types of artifacts. You’ll learn new approaches to segmentation such as edge detection and clustering. You’ll also analyze regions of interest and calculate properties such as size, orientation, and location. By the end of this course, you’ll be able to separate and analyze regions in your own images. You’ll apply your skills to segment an MRI image of a brain to separate different tissues. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your image processing tasks. You will be provided with free access to MATLAB for the duration of the course to complete your work. To be successful in this course you should have a background in basic math and some exposure to MATLAB. If you want to familiarize yourself with MATLAB check out the free, two-hour MATLAB Onramp. Experience with image processing is not required.
Introduction to Computer Vision
MathWorks (via Coursera)
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene. Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.
Automating Image Processing
MathWorks (via Coursera)
In this course, you will build on the skills acquired in Image Segmentation, Filtering, and Region Analysis to explore large sets of images and video files. It’s impractical to manually inspect results in large data sets. Automating image processing allows you to do your work more efficiently. At the end of this course, you’ll apply all the skills learned in this specialization to a final project. You’ll take the role of an engineer being asked to monitor traffic on a busy road. You’ll detect cars from a noisy video and analyze the results. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your image processing tasks. You will be provided with free access to MATLAB for the duration of the course to complete your work. To be successful in this course you should have a background in basic math and some exposure to MATLAB. If you want to familiarize yourself with MATLAB check out the free, two-hour MATLAB Onramp. Experience with image processing is not required.
Systems Engineering
MathWorks (via Coursera)
Systems Engineering provides a broad overview of how systems engineering helps you develop complex projects that meet program objectives in an efficient way. You will learn how system architectures are developed and described, how to communicate the needs, requirements, and constraints throughout the project, how to optimize the design through trade studies, and how to know the system does what it’s supposed to in the end. By the end of this short course, you will have a high-level understanding of important systems engineering concepts that you can use as a foundation for future learning.
Practical MATLAB Skills
MathWorks (via Coursera)
MATLAB is a powerful programming and numerical computing tool used widely across industries - from engineering and scientific research to data analysis. In this course, you’ll gain hands-on experience using MATLAB and will work with real-world data, build visualizations, and automate analysis. These skills serve as the foundation for advanced modeling and simulation in applications ranging from machine learning, deep learning, signal processing, communications, image processing, and control systems. Created by MathWorks, the developers of MATLAB, this course is regularly updated to reflect current best practices and new features. You’ll explore real examples and complete interactive exercises to answer questions like: • How far does a blue whale swim each day? • What’s the most popular pizza topping in a restaurant? • How does ride quality vary with car suspension design? • What factory failure causes the highest cost? • How does earthquake magnitude affect tsunami strength? No prior MATLAB or programming experience is required. For the duration of the course, you will have free access to MATLAB, allowing you to practice in the same environment used by top companies around the world. After completion, you’ll earn the "Foundational MATLAB" verified digital credential, which you can use to showcase your skills on LinkedIn or with employers. The course also serves as an entry to the MathWorks Certified MATLAB Associate certification for those seeking deeper validation of their capabilities with MATLAB for their career development.
Data Science Project: MATLAB for the Real World
MathWorks (via Coursera)
Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. You will choose your own pathway to answer key questions with the provided data. To complete the project, you must have mastery of the skills covered in other courses in the specialization. The project will test your ability to import and explore your data, prepare the data for analysis, train a predictive model, evaluate and improve your model, and communicate your results.
Creating Custom Apps in MATLAB
MathWorks (via Coursera)
Bring your data to life with interactive apps that make data exploration faster, clearer, and more impactful. In this course, you’ll use your MATLAB skills to build professional, user-friendly apps that make data analysis more engaging and accessible - no prior app design experience required. Through hands-on projects, you’ll design a complete app from the ground up: arranging visual components, adding interactive controls and plots, and writing code that automatically updates as users explore results. Along the way, you’ll learn how to create interfaces that help others gain insights without needing to write code. By the end of the course, you’ll have a polished, shareable app that demonstrates your ability to turn analysis into action—an in-demand skill for engineers, scientists, and analysts across industries. MATLAB access is provided so you can practice, experiment, and build real-world experience that advances your career.
Power Conversion for Electronic Devices
MathWorks (via Coursera)
Power electronics enable modern electrified technologies, from electric vehicles to renewable energy systems. In this course, you’ll learn how engineers design and model power converters to efficiently transfer and control electrical energy in real-world systems. Through guided exercises and practical examples, you’ll build and analyze models of common power converters, evaluate their performance, and explore how design decisions affect system behavior across different operating conditions. You’ll also examine key aspects of power converter operation, including how electrical signals are switched and regulated to achieve desired performance. These concepts help engineers design systems that meet efficiency and reliability requirements. You’ll receive a free Simulink and Simscape license to develop system-level models, run simulations, and compare different converter architectures before hardware is built. By the end of the course, you’ll be able to model, simulate, and analyze power converters and understand how they are used in modern electronic and electrified systems. These skills will help you contribute to the design and optimization of power electronics systems across a wide range of applications.
Data Science Companion
MathWorks (via Coursera)
The Data Science Companion provides an introduction to data science. You will gain a quick background in data science and core machine learning concepts, such as regression and classification. You’ll be introduced to the practical knowledge of data processing and visualization using low-code solutions, as well as an overview of the ways to integrate multiple tools effectively to solve data science problems. You will then leverage cloud resources from Amazon Web Services to scale data processing and accelerate machine learning model training. By the end of this short course, you will have a high-level understanding of important data science concepts that you can use as a foundation for future learning.
Low Code Image Segmentation
MathWorks (via Coursera)
This 1 hour course quickly teaches you how to segment images with MATLAB. You'll use apps to segment grayscale and color images. The apps generate code so you can apply the same steps to many images automatically. This course reuses content from our Image Processing for Engineering and Science specialization. To learn how to analyze your segmented images or to segment videos, we enourage you to enroll in the entire specialization.
Introduction to Deep Learning for Computer Vision
MathWorks (via Coursera)
Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges. By the end of this course, you will be able to: • Explain how deep learning networks find image features and make predictions • Retrain common models like GoogLeNet and ResNet for specific applications • Investigate model behavior to identify errors and determine potential fixes • Improve model performance by tuning hyperparameters • Complete the entire deep learning workflow in a final project For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
Designing and Simulating Physical Models
MathWorks (via Coursera)
As systems become more complex, engineers need to keep pace with the techniques for designing and testing the digital representations of their creations. This course equips you with the latest skills in modeling and simulation using Simscape™, an industry-standard tool integrated with Simulink® and prepares you to meet the changing needs of engineering roles in automotive, aerospace, and robotics sectors, ensuring you remain competitive and relevant in a rapidly advancing job market. You will have the opportunity to engage in practical, project-based learning that mirrors the challenges faced by top engineering firms. Through hands-on experience creating dynamic models and simulating real-world systems, you’ll develop skills in multi-domain physical modeling and component integration. You will use Simulink and Simscape throughout the course. Simulink is a multi-domain modeling and simulation environment for engineers and scientists who design controls, wireless, and other dynamic systems. Within the Simulink environment, Simscape is used to rapidly create models of real-world components like electric motors, batteries, quadcopters, and robot arms. You will receive free access to Simulink and Simscape for the duration of the course to complete your work. Whether you're an aspiring engineer or a seasoned professional, this course will enhance your skill set. Enroll now to gain skills that are in high demand across various engineering fields.
Advanced Deep Learning Techniques for Computer Vision
MathWorks (via Coursera)
Visual inspection and medical imaging are two applications that aim to find anything unusual in images. In this course, you’ll train and calibrate specialized models known as anomaly detectors to identify defects. You’ll also use advanced techniques to overcome common data challenges with deep learning. AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images. If you have too few images, you’ll generate synthetic training images using data augmentation for situations where acquiring more data is expensive or impossible. By the end of this course, you will be able to: • Train anomaly detection models • Generate synthetic training images using data augmentation • Use AI-assisted annotation to label images and video files • Import models from 3rd party tools like PyTorch • Describe approaches to using your model outside of MATLAB For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
Electric Motor Modeling and Control
MathWorks (via Coursera)
Electric motors power many modern technologies, from electric vehicles and industrial equipment to robotics and household appliances. In this course, you’ll learn how to model and control electric motors to achieve reliable and efficient performance in real-world systems. You will build and simulate models of permanent magnet synchronous motors and explore how electrical inputs produce mechanical motion. Using manufacturer data and pre-parameterized motor models, you’ll analyze motor behavior by measuring quantities such as voltage, current, and power. Through hands-on exercises, you’ll implement motor control strategies used in modern engineering applications. You’ll design open-loop and closed-loop control systems, configure pulse-width modulation to regulate motor speed, and implement field-oriented control for precise speed and torque control. You’ll receive a free Simulink and Simscape license to simulate motor performance, test control algorithms, and evaluate system behavior before hardware is built. By the end of the course, you’ll gain practical electric motor modeling and control skills used by engineers to design, analyze, and optimize motor-driven systems across industries such as electrification, robotics, industrial automation, and electric vehicles.
AI for Engineering: An Overview
MathWorks (via Coursera)
Modern engineering systems generate massive amounts of sensor data, simulations, logs, and performance metrics; far more than teams can manually analyze. AI helps engineers cut through this complexity, uncovering early warnings, hidden patterns, and system behaviors that traditional tools often miss. It accelerates testing, improves reliability, and supports better decisions across the entire product lifecycle. This course introduces how AI can complement engineering workflows in modeling and simulation, production, and real‑time operations. You’ll see how data‑driven reduced‑order models and physics‑informed machine learning speed up simulation; how virtual sensors extend what you can measure; and how computer vision, anomaly detection, predictive maintenance, and digital twins improve quality and reliability from design throughout the lifecycle. You'll also learn foundational responsible‑AI principles, such as explainability, interpretability, and observability, so you can evaluate AI‑generated insights and build trust in the systems you develop. By the end, you’ll be able to identify where AI can meaningfully support your work and confidently discuss opportunities and trade‑offs with technical teams. Enroll to gain a clear, high‑level perspective on AI’s role in engineering and begin exploring how it can enhance your work.
Deep Learning for Object Detection
MathWorks (via Coursera)
Detecting and locating objects is one of the most common uses of deep learning for computer vision. Applications include helping autonomous systems navigate complex environments, locating medical conditions like tumors, and identifying ready-to-harvest crops in agriculture. In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application. By the end of this course, you will be able to: • Explain how deep learning networks locate and classify objects in images • Retrain popular YOLO deep learning models for your application • Use a variety of metrics to evaluate prediction results • Visualize results to gain insights into model performance • Improve model performance by adjusting important model parameters • Analyze labeled images to identify and fix potential shortcomings in your data For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
Exploratory Data Analysis with MATLAB
MathWorks (via Coursera)
In this course, you will learn to think like a data scientist and ask questions of your data. You will use interactive features in MATLAB to extract subsets of data and to compute statistics on groups of related data. You will learn to use MATLAB to automatically generate code so you can learn syntax as you explore. You will also use interactive documents, called live scripts, to capture the steps of your analysis, communicate the results, and provide interactive controls allowing others to experiment by selecting groups of data. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background is required. To be successful in this course, you should have some knowledge of basic statistics (e.g., histograms, averages, standard deviation, curve fitting, interpolation). By the end of this course, you will be able to load data into MATLAB, prepare it for analysis, visualize it, perform basic computations, and communicate your results to others. In your last assignment, you will combine these skills to assess damages following a severe weather event and communicate a polished recommendation based on your analysis of the data. You will be able to visualize the location of these events on a geographic map and create sliding controls allowing you to quickly visualize how a phenomenon changes over time.
Reinforcement Learning
MathWorks (via Coursera)
This course provides an overview of reinforcement learning, a type of machine learning that has the potential to solve control system problems that are too difficult to solve with traditional techniques. You'll work through the basics of the reinforcement problem and how it differs from traditional control techniques. You'll also see how neural networks are used to represent unknown functions and how the agent uses rewards from the environment to train them.
Data Processing and Feature Engineering with MATLAB
MathWorks (via Coursera)
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed Exploratory Data Analysis with MATLAB. Throughout the course, you will merge data from different data sets and handle common scenarios, such as missing data. In the last module of the course, you will explore special techniques for handling textual, audio, and image data, which are common in data science and more advanced modeling. By the end of this course, you will learn how to visualize your data, clean it up and arrange it for analysis, and identify the qualities necessary to answer your questions. You will be able to visualize the distribution of your data and use visual inspection to address artifacts that affect accurate modeling.
Introduction to MATLAB Copilot
MathWorks (via Coursera)
MATLAB Copilot is a new AI-powered assistive tool that helps you enhance your coding efficiency, without the need for advanced prompt engineering. Designed for engineering and science professionals and students, this course explores practical applications of Generative AI in real-world contexts. You will use MATLAB Copilot to generate code explanations, uncover new insights, create and refine code drafts, add meaningful comments, and combine multiple assistive tools for a comprehensive approach to coding. Throughout the course, you'll get practical experience with guided activities and a hands-on project. By the end of the course, you'll confidently integrate generative AI features into your daily work to reduce coding and troubleshooting time, quickly find enhancements to code, and develop deeper trust in the code you develop. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your coding tasks. You will be provided free access to MATLAB for the course duration to complete your work. Enroll today to stay ahead as AI transforms the engineering landscape.
Predictive Modeling and Machine Learning with MATLAB
MathWorks (via Coursera)
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.
Battery Design and Management
MathWorks (via Coursera)
Batteries store and deliver energy that powers many electrified technologies, from electric vehicles and renewable energy systems to portable electronics. In this course, you’ll learn how to model, design, and manage batteries to ensure safe and reliable operation in real-world systems. Through hands-on exercises, you’ll design battery pack configurations to meet specific voltage and energy requirements and analyze how pack architecture affects system performance. You’ll also implement key components of a battery management system used to monitor and protect batteries. You’ll develop algorithms for safe charging, estimate the state of charge, and implement cell balancing to maintain safe and efficient battery operation. You’ll receive a free Simulink and Simscape license to model battery systems, test management strategies, and evaluate system behavior before hardware is built. By the end of the course, you’ll develop practical skills in battery modeling and management that engineers use to design and optimize the energy storage systems powering today’s electrified technologies.
Modeling and Simulation with Simulink
MathWorks (via Coursera)
As technology advances at an unprecedented pace, engineers are challenged to design complex systems with greater efficiency and accuracy. This course teaches you skills in system modeling, simulation, and analysis using Simulink®, a modeling and simulation environment used by the world's top engineering companies to design, simulate, and test systems before moving to hardware. The course provides practical, project-based learning that mirrors real-world challenges. You will gain hands-on experience in creating dynamic models, simulating real-world systems, and optimizing performance, making you a valuable asset to any engineering team. Key skills you'll develop include system design, control systems, and simulation analysis. Throughout the course, you will use Simulink, a block diagram environment used to design systems with multidomain models, simulate before moving to hardware, and deploy without writing code. You will be provided with a free Simulink license to complete your work in the course. Whether you're an aspiring engineer or a seasoned professional, you'll acquire skills that are highly sought after in engineering fields such as automotive, aerospace, robotics, and more.
Object Tracking and Motion Detection with Computer Vision
MathWorks (via Coursera)
In the third and final course of the Computer Vision for Engineering and Science specialization, you will learn to track objects and detect motion in videos. Tracking objects and detecting motion are difficult tasks but are required for applications as varied as microbiology and autonomous systems. To track objects, you first need to detect them. You’ll use pre-trained deep neural networks to perform object detection. You’ll also use optical flow to detect motion and use the results to detect moving objects. At the end of this course, you’ll apply all the skills learned in this specialization to a final project. You’ll take the role of an engineer being asked to track cars on a busy highway with the added challenge of counting each vehicle and its direction. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this specialization, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.
Introduction to Image Processing
MathWorks (via Coursera)
In this introduction to image processing, you'll take your first steps in accessing and adjusting digital images for analysis and processing. You will load, save, and adjust image size and orientation while also understanding how digital images are recognized. You will then perform basic segmentation and quantitative analysis. Lastly, you will enhance the contrast of images to make objects of interest easier to identify. By the end of the course, you’ll apply your segmentation skills to identify regions of interest, such as the amount of surface water from satellite images. This introduction to image processing will give you the foundation you need to conduct more advanced work on this topic. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science and provides the capabilities you need to accomplish your image processing tasks. You will be provided with free access to MATLAB for the duration of the course to complete your work. To be successful in this course you should have a background in basic math and some exposure to MATLAB. If you want to familiarize yourself with MATLAB check out the free, two-hour MATLAB Onramp. Experience with image processing is not required.