Using image processing, machine learning and deep learning methods to build computer vision applications using popular frameworks such as OpenCV and TensorFlow in Python.
Using the state-of-the-art YOLOv3 object detection for real-time object detection, recognition and localization in Python using OpenCV and PyTorch.
Using K-Means Clustering unsupervised machine learning algorithm to segment different parts of an image using OpenCV in Python.
Learning how to detect contours in images for image segmentation, shape analysis and object detection and recognition using OpenCV in Python.
Detecting shapes, lines and circles in images using Hough Transform technique with OpenCV in Python. Hough transform is a popular feature extraction technique to detect any shape within an image.
Learning how to apply edge detection in computer vision applications using canny edge detector algorithm with OpenCV in Python.
Learn what is transfer learning and how to use pre trained MobileNet model for better performance to classify flowers using TensorFlow in Python.
Building and training a model that classifies CIFAR-10 dataset images that were loaded using Tensorflow Datasets which consists of airplanes, dogs, cats and other 7 objects using Tensorflow 2 and Keras libraries in Python.