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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

4 weeks

Real-world Projects

Completion Certificate

Last Updated February 3, 2022

Prerequisites:

No experience required

Course Lessons

Lesson 1

Welcome to Computer Vision

Welcome to the Computer Vision Nanodegree program!

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

Photo of Cezanne Camacho

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.

Photo of Alexis Cook

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.

Photo of Luis Serrano

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.

Taught By The Best

Photo of Cezanne Camacho

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.

Photo of Alexis Cook

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.

Photo of Luis Serrano

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.

Get Started Today

Introduction to Computer Vision