Courses

Prepare for a career in the rapidly expanding field of computer vision. The ability to extract meaningful information from visual data is crucial for efficiently developing smart monitoring systems, enhancing medical diagnostics, and powering the next generation of autonomous vehicles. This program is designed to transform those with little or no prior image data experience into proficient computer vision practitioners by completing several hands-on projects with real-world applications. By the end of the program, you will have the skills necessary to automate image processing tasks, detect and track motion, train deep learning models for image classification and object detection tasks, and implement advanced deep learning techniques like data augmentation and AI-assisted annotation. Possessing these skills will prepare you to keep pace with modern systems, which increasingly incorporate cameras into their designs. Throughout the program, you will use MATLAB, an industry-standard platform known for its user-friendly interface and robust functions that simplify complex algorithms. The intuitive apps and functions mean that you don't need to be a programmer or math expert to achieve success. You will leverage MATLAB capabilities to rapidly prototype and iterate on solutions, giving you a competitive edge in the job market and the confidence to push the boundaries of what's possible in computer vision and deep learning.

In this course, Azure AI Engineer Associate (AI-102): Computer Vision Solutions, you’ll learn to utilize all the vision capabilities of Azure for your data. First, you’ll explore how to apply AI to your images including classification and text extraction. Next, you’ll discover how to train your own model for enhanced image abilities. Finally, you’ll learn how to bring AI abilities to video media. When you’re finished with this course, you’ll have the skills and knowledge of computer vision AI abilities needed to light up new knowledge and capabilities for your custom applications.

Learn how to implement computer vision solutions in Azure AI and prepare for the Azure AI Engineer Associate (AI-102) exam.

Learn how synthetic data generation can revolutionize industrial computer vision deployment from a Meta Applied Research Scientist in this 18-minute conference talk. Discover why 54% of AI projects fail at the proof-of-concept stage due to data acquisition challenges, particularly in manufacturing and industrial automation where collecting images for object detection can take 6-12 months. Explore how generative AI transforms this bottleneck by enabling synthetic data creation that accelerates development cycles and enables faster deployment of robust AI models in production environments. Gain insights from an expert who works with billion-scale datasets to train massive multimodal foundational models at Facebook AI Research, with previous experience at Citadel as a Quantitative Researcher and Amazon Alexa AI on large-scale multimodal models and Embodied AI applications. Understand the practical applications of self-supervised learning and multimodal AI in solving real-world industrial computer vision challenges.

Explore a comprehensive talk on detecting masked faces using Deep Learning during the pandemic era. Learn from Vladimir Iglovikov, a Sr. Computer Vision Engineer at Lyft and Kaggle Grandmaster, as he shares his approach to verifying face mask usage. Discover insights into object detection, face detection, and the importance of data sets in developing effective models. Gain practical knowledge through real-time examples, understand common mistakes, and explore key points for training and implementing web applications. This informative session covers the entire process from problem definition to solution implementation, offering valuable insights for machine learning enthusiasts and professionals alike.

The Computer Vision specialization takes you from the foundations of computer vision to the cutting edge of multimodal AI. Whether you're just starting out or looking to deepen your expertise, you'll gain the skills to build intelligent systems that interpret and generate visual data—just like today’s most advanced AI models.

Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models.

Build convolutional neural networks with TensorFlow and Keras.Create your first computer vision model with Keras.Discover how convnets create features with convolutional layers.Learn more about feature extraction with maximum pooling.Explore two important parameters: stride and padding.Design your own convnet.Boost performance by creating extra training data.Use Kaggle's free TPUs to make a submission to the Petals to the Metal competition!Use Kaggle's free TPUs to make a submission to the Cassava Leaf Disease Classification competition.

The course will have a comprehensive coverage of theory and computation related to imaging geometry, and scene understanding. It will also provide exposure to clustering, classification and deep learning techniques applied in this area. INTENDED AUDIENCE: Computer Science and Engineering, Electronics Engineering, Electrical Engineering PRE-REQUISITES: Liner Algebra, Vector Calculus, Data Structures and Programming

Dive into a comprehensive 3.5-hour course on computer vision, exploring machine learning fundamentals, neural networks, and advanced architectures. Learn about supervised and unsupervised learning, data processing techniques, and model evaluation. Discover key computer vision applications and image representation methods. Explore essential neural network components, including neurons, activation functions, and convolutions. Examine popular architectures like LeNet, AlexNet, VGG, and ResNet. Delve into object detection techniques, including bounding boxes, anchor boxes, and models such as R-CNN and YOLO. Investigate semantic segmentation and fully convolutional networks, culminating with the U-Net architecture. Gain hands-on experience using Jupyter Notebooks on SageMaker to reinforce your learning.

Dive into a comprehensive 15-hour course on Computer Vision offered by Eberhard Karls University of Tübingen. Explore the fundamentals of image formation, structure-from-motion techniques, stereo reconstruction methods, and probabilistic graphical models. Learn about the history of computer vision, geometric and photometric image formation, and image sensing pipelines. Discover advanced topics such as two-frame structure-from-motion, bundle adjustment, block matching, and Siamese networks for stereo reconstruction. Gain insights into Markov Random Fields, factor graphs, and belief propagation in probabilistic graphical models. Apply these concepts to real-world problems like multi-view reconstruction and optical flow. Investigate shape-from-X techniques, including shape-from-shading, photometric stereo, and volumetric fusion. Master parameter estimation and deep structured models in graphical model learning.

Explore fundamental concepts and advanced techniques in computer vision through this comprehensive summer school lecture from Johns Hopkins University's Center for Language & Speech Processing. Delve into core computer vision principles including image processing, feature extraction, object detection, and recognition algorithms. Learn about modern deep learning approaches to visual understanding, convolutional neural networks, and their applications in real-world scenarios. Examine cutting-edge research developments in areas such as image segmentation, visual tracking, and multi-modal learning that combines vision with other modalities. Gain insights into practical implementation challenges and solutions used in contemporary computer vision systems. Discover how computer vision techniques are applied across various domains including robotics, autonomous systems, medical imaging, and multimedia analysis. Understand the mathematical foundations underlying computer vision algorithms and their computational requirements for different applications.

Learn the basics of computer vision by applying a typical workflow—tracking-by-detection—to video of turtles crawling towards the sea. You will learn about the role of features in computer vision, how to label data, train an object detector, and track wildlife in video.

In this tutorial series, we would be working on hands-on projects using OpenCV and Python. We shall begin with controlling volume using a hand gesture to change the volume on a computer and proceed to even work on a virtual calculator using openCV.

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. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. * A free license to install MATLAB for the duration of the course is available from MathWorks.

Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python! What you'll learn: Understand the basic intuition about Cascade and HOG classifiers to detect facesImplement face detection using OpenCV and Dlib libraryLearn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of peopleCompare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)Detect faces using images and the webcamUnderstand the basic intuition about LBPH algorithm to recognize facesImplement face recognition using OpenCV and Dlib libraryRecognize faces using images and the webcamUnderstand the basic intuition about KCF and CSRT algorithms to perform object trackingLearn how to track objects in videos using OpenCV libraryLearn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot moreImplement dense neural networks to classify imagesLearn how to extract pixels and features from images in order to build neural networksLearn the theory behind convolutional neural networks and implement them using Python and TensorFlowImplement transfer learning and fine tuning to get incredible results when classifying imagesUse convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutralCompress images using linear and convolutional autoencodersDetect objects in images in videos using YOLO, one of the most powerful algorithms todayRecognize gestures and actions in videos using OpenCVLearn how to create hallucinogenic images with Deep DreamLearn how to revive famous artists with style transferCreate images that don't exist in the real world with GANs (Generative Adversarial Networks)Implement image segmentation do extract useful information from images and videos Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques. If you have never heard about computer vision, at the end of this course you will have a practical overview of all areas. Below you can see some of the content you will implement:Detect faces in images and videos using OpenCV and Dlib librariesLearn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib librariesTrack objects in videos using KCF and CSRT algorithmsLearn the whole theory behind artificial neural networks and implement them to classify imagesImplement convolutional neural networks to classify imagesUse transfer learning and fine tuning to improve the results of convolutional neural networksDetect emotions in images and videos using neural networksCompress images using autoencoders and TensorFlowDetect objects using YOLO, one of the most powerful techniques for this taskRecognize gestures and actions in videos using OpenCVCreate hallucinogenic images using the Deep Dream techniqueCombine style of images using style transferCreate images that don't exist in the real world with GANs (Generative Adversarial Networks)Extract useful information from images using image segmentationYou are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab

Learn OpenCV by reading, writing, and displaying images, Car, facial and eye detection using HAAR Cascade Classifier, and Pedestrian detection from videos

Welcome to this course on OpenCV Python Tutorial OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python. it is Open Source and free. opencv is easy to use and install. We are going to open Image/Video Webcam in OpenCV and then work on corner and edge detections along with histogram equalization.

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.

Introduction to Computer Vision, make vision apps What you'll learn: Do image processingUse basic Computer Vision techniquesBuild: Image Similarity appBuild: Search Engine appBuild: Face Detection appBuild: Object Detection app using templatesBuild: Object Detection app using Keypoints Introduction course to Computer Vision with Python. Make Computer Vision Apps?Learn Computer Vision theory?Build a strong portfolio with Computer Vision & Image Processing Projects?Looking to add Computer Vision algorithms in your current software project ?Whatever be your motivation to learn Computer Vision, I can assure you that you’ve come to the right course. This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to a Computer Vision expert in a few weeks.The most difficult concepts are explained in plain and simple manner using code examples. We will build: Multimedia appsImage Similarity appsObect Detection appsFace detection appsReverse Image Search app I personally guarantee this is the number one course for you. This may not be your first OpenCV course, but trust me - It will definitely be your last. I assure you, that you will receive fast, friendly, responsive support by email, and on the Udemy. Don't believe me? I offer a full money back guarantee, so long as you request it within 30 days of your purchase of the course. Also the courseis updated on a regular basis to add more new and exciting content. Join the course right now. So what are you waiting for ? Let’s meet at the other side of the course.