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Our Courses
Computer Vision Basics
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence.
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Course by
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13 hours
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English
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types.
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Course by
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Self Paced
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English
Computer Vision for Engineering and Science
Cameras are an integral component in many new technologies. Autonomous systems use cameras to navigate their environment, while doctors use small cameras to help guide minimally invasive surgical techniques. It is essential that engineers use computer vision techniques to extract information from these types of images and videos. In this specialization, you’ll gain the computer vision skills underpinning many of today’s top jobs.
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Course by
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Self Paced
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English
Introduction to Intel® Distribution of OpenVINO™ toolkit for Computer Vision Applications
Welcome to the Introduction to Intel® Distribution of OpenVINO™ toolkit for Computer Vision Applications course!
This course provides easy access to the fundamental concepts of the Intel Distribution of OpenVINO toolkit. Throughout this course, you will be introduced to demos, showcasing the capabilities of this toolkit.
With the skills you acquire from this course, you will be able to describe the value of tools and utilities provided in the Intel Distribution of OpenVINO toolkit, such as the model downloader, model optimizer and inference engine.
Who this class is for:
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Course by
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Self Paced
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English
Computer Vision - Image Basics with OpenCV and Python
In this 1-hour long project-based course, you will learn how to do Computer Vision on images with OpenCV and Python using Jupyter Notebook.
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Course by
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Self Paced
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2 hours
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English
Deep Learning Applications for Computer Vision
In this course, you’ll be learning about Computer Vision as a field of study and research. First we’ll be exploring several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. Then we’ll introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results and discuss advantages and drawbacks of both types of methods. We'll use tutorials to let you explore hands-on some of the modern machine learning tools and software libraries.
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Course by
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Self Paced
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23 hours
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English
AI and Disaster Management
In this course, you will be introduced to the four phases of the disaster management cycle; mitigation, preparation, response, and recovery. You’ll work through two case studies in this course. In the first, you will use computer vision to analyze satellite imagery from Hurricane Harvey in 2017 to identify damage in affected areas. In the second, you will use natural language processing techniques to explore trends in aid requests in the aftermath of the 2010 earthquake in Haiti.
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Course by
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Self Paced
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12 hours
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English
Camera and Imaging
This course covers the fundamentals of imaging – the creation of an image that is ready for consumption or processing by a human or a machine. Imaging has a long history, spanning several centuries. But the advances made in the last three decades have revolutionized the camera and dramatically improved the robustness and accuracy of computer vision systems. We describe the fundamentals of imaging, as well as recent innovations in imaging that have had a profound impact on computer vision. This course starts with examining how an image is formed using a lens camera.
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Course by
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Self Paced
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17 hours
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English
Recognizing Shapes in Images with OpenCV
In this 1.5 hour long project-based course, you will apply computer vision techniques to process images, extract useful features and detect shapes using Hough transforms.
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Course by
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Self Paced
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3 hours
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English
Object Localization with TensorFlow
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection.
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Course by
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Self Paced
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3 hours
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English
Computer Vision and Image Processing Fundamentals
Learn about computer vision, one of the most exciting fields in machine learning. artificial intelligence and computer science.
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Course by
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Self Paced
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10
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English
Convolutional Neural Networks
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
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Course by
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Self Paced
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36 hours
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English
Introduction to Computer Vision and Image Processing
Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries. As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. This is a hands-on course and involves several labs and exercises.
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Course by
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Self Paced
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22 hours
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English
Computer Vision Fundamentals with Google Cloud
This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
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Course by
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Self Paced
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19 hours
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English
Convolutional Neural Networks in TensorFlow
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the DeepLearning.AI TensorFlow Developer Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1.
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Course by
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Self Paced
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17 hours
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English
Object Tracking and Motion Detection with Computer Vision
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.
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Course by
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Self Paced
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14 hours
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English
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks.
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Course by
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31 hours
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English
Browser-based Models with TensorFlow.js
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. In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam. This Specialization builds upon our TensorFlow in Practice Specialization.
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Course by
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Self Paced
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19 hours
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English
Applied AI with DeepLearning
>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area
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Course by
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Self Paced
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25 hours
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English
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.
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Course by
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Self Paced
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27 hours
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English