The goal of computer vision is to derive descriptive information about a scene by computer analysis of images of the scene. Vision algorithms can often serve as computational models for biological visual processes, and they also have many practical uses; this course treats computer vision as a subject in its own right. Vision problems are often ill-defined, ill-posed, or computationally intractable; nevertheless, successes have been achieved in many specific areas — document processing and industrial inspection.

In this course, you will understand images and how to prepare them. You will learn about the role of programs and data in the field of Artificial Intelligence. You will get a detailed knowledge of the two main technologies: Deep Learning and Convolutive Neural networks. You will come to know the advantages and disadvantages of deep learning. You will have a good basic level understanding of AI as well as ML.

With this course, you will begin to learn how to deal with images and what you can do about it, avoid data mistakes in a machine learning setting, know what Deep Learning is and finally build some cool TensorFlow applications like objects detection or face recognition.