New!
Time
1 Three-Month Term
Study 10-15 hrs/week and complete in 3 months.
Classroom Opens
February 26, 2019
In Collaboration with
Why Take This Nanodegree Program?
From computer graphics to social robotics to autonomous vehicles, computer vision is powering world-changing new technologies. In this program, you’ll write code to perform everything from facial recognition to scene-understanding to object tracking; by the end of this program, you’ll have a broad portfolio of applications that you’ve built!
Learn the Most Cutting-Edge Techniques
Computer vision is a rapidly growing field that powers a variety of emerging technologies—from facial recognition to augmented reality to self-driving cars. Learn the latest deep learning architectures and image processing techniques today!
Built in Collaboration with Industry
We collaborated with industry leaders from NVIDIA to Affectiva to build a program that showcases how computer vision is being applied on the front-lines of technology today.
Code Your Own Computer Vision Apps
You’ll learn how to program computer vision techniques in Python, and then use that knowledge to create your own applications! You’ll complete three major computer vision projects, and build a strong portfolio in the process.
Personalized Project Reviews
Get personalized feedback on your computer vision projects from a team of technical reviewers. The invaluable reviews you receive mirror the experience of working on a team of engineers and mentors, and this feedback offers you unique and actionable insights as to how you should develop code!
What You Will Learn
Download SyllabusSyllabus
Foundations of Computer Vision
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Prerequisite Knowledge
This program requires experience with Python, statistics, machine learning, and deep learning.See detailed requirements.
Introduction to Computer Vision
Master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks.
Facial Keypoint DetectionAdvanced Computer Vision and Deep Learning
Learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application.
Automatic Image CaptioningObject Tracking and Localization
Learn how to locate an object and track it over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight.
Landmark Detection & Tracking
“A lot of the future of search is going to be about pictures instead of keywords. Computer vision technology is going to be a big deal.”— Ben Silbermann, CEO, Pinterest
Learn with the best
Sebastian Thrun
Udacity President
Sebastian Thrun is a scientist, educator, inventor, and entrepreneur. Prior to founding Udacity, he launched Google’s self-driving car project.
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
Content Developer
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.
Juan Delgado
Content Developer
Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.
Jay Alammar
Content Developer
Jay has a degree in computer science, loves visualizing machine learning concepts, and is the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
Ortal Arel
Content Developer
Ortal Arel has a PhD in Computer Engineering, and has been professor and researcher in the field of applied cryptography and embedded platforms. She has worked on design and analysis of intelligent algorithms for high-speed custom digital architectures.
Luis Serrano
Content Developer
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.
Student Reviews
Top Reviews
(71)
5 stars
67.6%
4 stars
16.9%
3 stars
11.3%
2 stars
2.8%
1 stars
1.4%
Carlos O.
One of the most incredible NDs ever! I've being learning more than a regular graduation. I am full of perspectives.
Ho Yiu C.
Very practical, I learnt a lot from this experience.
Akira K.
It was very good that I could get familiarized with OpenCV library, and deep learning (CNN/RNN) with PyTorch. It was also very good that this program covers the latest topics, such as YOLO and SLAM. Lesson explanations are very carefully made and I could learn difficult concepts step by step.
Christian O.
Great program, can recommend this to everyone.
Ramy Raafat Mohamed Fawzy Ahmed W.
The program exceeded my expectation to be honest, I learned a lot and it was lots of fun. What was really good as well was the extra feedback on how your models can be improved. I appreciated the course a lot and thanks to everyone who participated in the program on the teaching/logistics side, it was a great experience.
New
Computer Vision
$999 USD
total
Learn the essentials of computer vision, including image transformation, neural network architectures, and object recognition
