Image processing enhances images or extracts useful information from them. In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow.

In this module, you will Learn About the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbours, Logistic regression, SoftMax Regression and Support Vector Machines. Finally, you will learn about Image features.

In this module, you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural Network (CNN). You will learn about different components such as Layers and different types of activation functions such as ReLU. You also get to know the different CNN Architecture such as ResNet and LenNet.

In this module, you will learn about object detection with different methods. The first approach is using the Haar Cascade classifier, the second one is to use R-CNN and MobileNet.

In the final week of this course, you will build and evaluate an image classifier using transfer learning. For this project, you will train your custom model on labeled images and test its performance.