Learn Computer Vision Masterclass Free Video Course

Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python!

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What you will learn from this Course:

  • 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 for this Course:

  • Programming logic
  • Basic Python programming

Description:

PC Vision is a subarea of Artificial Intelligence zeroed in on making frameworks that can interact, dissect and distinguish visual information along these lines to the natural eye. There are numerous business applications in different divisions, for example, security, showcasing, dynamic and creation. Cell phones use Computer Vision to open gadgets utilizing face acknowledgement, self-driving vehicles use it to distinguish walkers and stay away from different vehicles, just as surveillance cameras use it to recognize whether there are individuals in the climate for the caution to be set off.

In this course, you will learn all you require to know to get into this world. You will gain proficiency with the bit by bit execution of the 14 (fourteen) primary PC vision methods. In the event that you have never found out about PC vision, toward the finish of this course you will have a useful outline, all things considered. Beneath you can see a portion of the substance you will execute:

  • Identify faces in pictures and recordings utilizing OpenCV and Dlib libraries
  • Figure out how to prepare the LBPH calculation to perceive faces, likewise utilizing OpenCV and Dlib libraries
  • Track objects in recordings utilizing KCF and CSRT calculations
  • Gain proficiency with the entire hypothesis behind fake neural organizations and carry out them to characterize pictures
  • Carry out convolutional neural organizations to order pictures
  • Use move learning and calibrating to work on the aftereffects of convolutional neural organizations
  • Identify feelings in pictures and recordings utilizing neural organizations
  • Pack pictures utilizing autoencoders and TensorFlow
  • Identify objects utilizing YOLO, perhaps the most remarkable strategies for this undertaking
  • Perceive signals and activities in recordings utilizing OpenCV
  • Make stimulating pictures utilizing the Deep Dream strategy
  • Join style of pictures utilizing style move
  • Make pictures that don’t exist in reality with GANs (Generative Adversarial Networks)
  • Concentrate valuable data from pictures utilizing picture division

You will become familiar with the essential instinct with regards to the calculations and execute some tasks bit by bit utilizing 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

Course content:

  • Introduction
  • Face Detection
  • Face Recognition
  • Object Tracking
  • Neural Networks For Image Classification
  • Convolutional Neural Networks For Image Classification
  • Transfer Learning and Fine turning
  • Neural Networks For Classification Of Emotions
  • Autoencoders
  • Object Detection With YOLO
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