Lesson 1
Welcome to Computer Vision
Welcome to the Computer Vision Nanodegree program!
Introduction to Computer Vision
Course
Master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks
Master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks
Advanced
4 weeks
Real-world Projects
Completion Certificate
Last Updated April 20, 2024
Object detection • PyTorch • Image classification • Data augmentation
Object-oriented programming basics • Intermediate Python • Neural network basics
Course Lessons
Lesson 2
Image Representation & Classification
Learn how images are represented numerically and implement image processing techniques, such as color masking and binary classification.
Lesson 3
Convolutional Filters and Edge Detection
Learn about frequency in images and implement your own image filters for detecting edges and shapes in an image. Use a computer vision library to perform face detection.
Lesson 4
Types of Features & Image Segmentation
Program a corner detector and learn techniques, like k-means clustering, for segmenting an image into unique parts.
Lesson 5
Feature Vectors
Learn how to describe objects and images using feature vectors.
Lesson 6
CNN Layers and Feature Visualization
Define and train your own convolution neural network for clothing recognition. Use feature visualization techniques to see what a network has learned.
Lesson 7 • Project
Project: Facial Keypoint Detection
Apply your knowledge of image processing and deep learning to create a CNN for facial keypoint (eyes, mouth, nose, etc.) detection.
Taught By The Best
Cezanne Camacho
Curriculum Lead
Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she's applied computer vision and deep learning to medical diagnostic applications.
Alexis Cook
Curriculum Lead
Alexis is an applied mathematician with a Masters in Computer Science from Brown University and a Masters in Applied Mathematics from the University of Michigan. She was formerly a National Science Foundation Graduate Research Fellow.
Luis Serrano
Instructor
Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.
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