Computer Vision Masterclass
What you'll learn
- Understand the basic intuition about Cascade and HOG classifiers to detect faces
- Implement face detection using OpenCV and Dlib library
- Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
- Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)
- Detect faces using images and the webcam
- Understand the basic intuition about LBPH algorithm to recognize faces
- Implement face recognition using OpenCV and Dlib library
- Recognize faces using images and the webcam
- Understand the basic intuition about KCF and CSRT algorithms to perform object tracking
- Learn how to track objects in videos using OpenCV library
- Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot more
- Implement dense neural networks to classify images
- Learn how to extract pixels and features from images in order to build neural networks
- Learn the theory behind convolutional neural networks and implement them using Python and TensorFlow
- Implement transfer learning and fine tuning to get incredible results when classifying images
- Use convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutral
- Compress images using linear and convolutional autoencoders
- Detect objects in images in videos using YOLO, one of the most powerful algorithms today
- Recognize gestures and actions in videos using OpenCV
- Learn how to create hallucinogenic images with Deep Dream
- Learn how to revive famous artists with style transfer
- Create 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
Requirements
- Programming logic
- Basic Python programming
Description
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 libraries
Learn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib libraries
Track objects in videos using KCF and CSRT algorithms
Learn the whole theory behind artificial neural networks and implement them to classify images
Implement convolutional neural networks to classify images
Use transfer learning and fine tuning to improve the results of convolutional neural networks
Detect emotions in images and videos using neural networks
Compress images using autoencoders and TensorFlow
Detect objects using YOLO, one of the most powerful techniques for this task
Recognize gestures and actions in videos using OpenCV
Create hallucinogenic images using the Deep Dream technique
Combine style of images using style transfer
Create images that don't exist in the real world with GANs (Generative Adversarial Networks)
Extract useful information from images using image segmentation
You are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab
Who this course is for:
- Beginners who are starting to learn Computer Vision
- Undergraduate students who are studying subjects related to Artificial Intelligence
- People who want to solve their own problems using Computer Vision
- Students who want to work in companies developing Computer Vision projects
- People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve
- Anyone interested in Artificial Intelligence or Computer Vision
- Data scientists who want to grow their portfolio
- Professionals who want to understand how to apply Computer Vision to real projects
Instructors
- 4.6 Instructor Rating
- 45,680 Reviews
- 222,555 Students
- 111 Courses
Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.
- 4.5 Instructor Rating
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Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!
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Hi there,
We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
The Real People at Ligency
- 4.6 Instructor Rating
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Olá, eu me chamo Gabriel Alves e sou formado em Ciência da Computação pela Universidade do Contestado (UnC) de Porto União - SC. Também possuo Curso Técnico em Informática pelo Colégio Técnico de União da Vitória (COLTEC), concluído em 2014. Trabalho como desenvolvedor há 10 anos, mas já lido com programação há mais de 12 anos. Em meus projetos faço o uso de várias linguagens, especialmente Python, a qual tenho preferência para utilizar no desenvolvimento das minhas pesquisas relacionadas a Inteligência Artificial e Aprendizagem de Máquina. Sou desde sempre apaixonado pela computação e por temas que envolvem ciência, tecnologia e arte.
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We are an on-line platform focused on courses on Artificial Intelligence, Machine Learning and Data Science. Our goal is to offer easy-to-understand theoretical and practical content, so that professionals from all areas can understand the benefits that AI can bring to their businesses. We are established in Brazil since 2018 and we have already published more than 90 courses in English and Portuguese on the Udemy platform.