Estimated Time
3 Months
At 10-15 hrs/week
Enroll by
April 14, 2020
Get access to classroom immediately on enrollment
Prerequisites
Python, Statistics, Machine Learning, & Deep Learning
In collaboration with
In collaboration with
What You Will Learn
Download SyllabusSyllabus
Foundations of Computer Vision
Hide details
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
All Our Programs Include
Real-world projects from industry experts
With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Technical mentor support
Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track.
Personal career coach and career services
You’ll have access to career coaching sessions, interview prep advice, and resume and online professional profile reviews to help you grow in your career.
Flexible learning program
Get a custom learning plan tailored to fit your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
Program OfferingsFull list of offerings included:
Enrollment includes:
Student Services
Technical mentor support
New
Student community
Improved
Career services
Personal career coaching
New
Interview preparations
Resume services
Github review
LinkedIn profile review
Resume shared with 95+ employers
New
Class Content
Content co-created with Affectiva
Real-world projects
Project reviews
Project feedback from experienced reviewers
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Experienced Project Reviewers
Technical Mentor Support
Personal Career Coach
Get personalized feedback on your projects
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
Succeed with Personalized Services
We provide services customized for your needs at every step of your learning journey to ensure your success!
Project Reviewers
Technical Mentor Support
Career Coaching
Get personalized feedback on your projects
Reviews By the numbers
2000+ project reviewers
1.8M projects reviewed
4.85/5 reviewer ratings
3 hour avg project review turnaround time
Reviewer Services
- Personalized feedback
- Unlimited submissions and feedback loops
- Practical tips and industry best practices
- Additional suggested resources to improve
Learn with the best
Sebastian Thrun
Instructor
As the founder and president of Udacity, Sebastian’s mission is to democratize education. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.
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.
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
Instructor
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
Curriculum Lead
Ortal Arel has a PhD in Computer Engineering, and has been a professor and researcher in the field of applied cryptography. She has worked on design and analysis of intelligent algorithms for high-speed custom digital architectures.
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.
Student Reviews
(222)
Pranjal C.
Exceptionally well rounded and wholesome introduction to Computer Vision methods going from the very basic 'classical' OpenCV based techniques to all the way to sophisticated Deep Learning based applications. However, I do wish that Udacity 'updates' this course soon as the state of the art techniques discussed have changed a lot since 2017-18. After the completion of this ND, you'd be deft with state of the art advances in Computer Vision that have happened around till late 2017/early 2018. I would still give this ND a 5-star rating because it is so well made!
Toure L.
I've found the program more challenging than I expected. That's a good thing. My mentor has been very helpful and generally responds very promptly. I think the online help could be improved. The chat room with other students hasn't been very helpful. The course materials are pretty good and I like the idea of extra project activities to dig deeper into the topics. It took me so much time to complete the first project that I did not have time to do any of the extra activities. this time.
Arunava C.
Its pretty good. Just expected a little more portions where we are supposed to write code and handle things on my own. While it has been perfect for me, a guy trying to do complete 2 Nanodegrees (CVND and DRLND) and have full-time job and a few other things. But a bit more letting the students handle, might let the ones who are just getting started with these a bit more experienced. Just a thought. Keep rocking!
Christopher O.
This program is excellent for teaching the fundamentals of computer vision. The projects are interesting and fun too. I recommend it to anyone that has an interest in CV for robotics or AI. Some background knowledge is already assumed, but they provide all of the necessary information in the lectures for escential learning!
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.
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.
Related Nanodegree Programs
Program Details
Why should I enroll in this program?
The demand for engineers with computer vision and deep learning skills far exceeds the current supply. This program offers a unique opportunity to develop these in-demand skills and is for anyone seeking to launch or advance their skills in modern computer vision techniques. You’ll complete several computer vision applications using a combination of Python, computer vision, and deep learning libraries that will serve as portfolio pieces that demonstrate the skills you’ve acquired.
What jobs will this program prepare me for?
This program is designed to build on your skills in machine learning and deep learning. As such, it doesn't prepare you for a specific job, but expands your skills in the computer vision domain. These skills can be applied to various applications such as image and video processing, automated vehicles, smartphone apps, and more.
How do I know if this program is right for me?
If you’re new to Computer Vision, and eager to explore applications like facial recognition and object tracking, the Computer Vision Nanodegree program is an ideal choice. The curriculum introduces you to image analysis with Python and OpenCV, then goes on to cover deep learning techniques that can be applied to a variety of image classification and regression tasks. Over the course of the program, you’ll leverage your Python coding experience to build a broad portfolio of applications that showcase your newly-acquired Computer Vision skills.
PROGRAM OVERVIEW - WHY SHOULD I TAKE THIS PROGRAM?
Do I need to apply? What are the admission criteria?
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
What are the prerequisites for enrollment?
You must have completed a course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. Additionally, you should have the following knowledge: Intermediate Python programming knowledge, including:
- Strings, numbers, and variables
- Statements, operators, and expressions
- Lists, tuples, and dictionaries
- Conditions, loops
- Generators & comprehensions
- Procedures, objects, modules, and libraries
- Troubleshooting and debugging
- Research & documentation
- Problem solving
- Algorithms and data structures
Basic shell scripting:
- Run programs from a command line
- Debug error messages and feedback
- Set environment variables
- Establish remote connections
Basic statistical knowledge, including:
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
Intermediate differential calculus and linear algebra, including:
- Derivatives & Integrals
- Series expansions
- Matrix operations through eigenvectors and eigenvalues
If I don’t meet the requirements to enroll, what should I do?
We have a number of courses and programs we can recommend that will help prepare you for the program, depending on the areas you need to address. For example:
ENROLLMENT AND ADMISSION
How is this Nanodegree program structured?
The Computer Vision Nanodegree program is comprised of content and curriculum to support three (3) projects. We estimate that students can complete the program in three (3) months working 10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
How long is this Nanodegree program?
Access to this Nanodegree program runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our Nanodegree programs.
Can I switch my start date? Can I get a refund?
Please see the Udacity Program FAQs for policies on enrollment in our programs.
I have graduated from the Computer Vision Nanodegree program but I want to keep learning. Where should I go from here?
Many of our graduates continue on to our Artificial Intelligence Nanodegree program, Natural Language Processing Nanodegree Program, Robotics Engineer Nanodegree program, and our Self-Driving Car Engineer Nanodegree programs. Feel free to explore other Nanodegree program options as well.
TUITION AND TERM OF PROGRAM
What software and versions will I need in this program?
You will need a computer running a 64-bit operating system (most modern Windows, OS X, and Linux versions will work) with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.5 and supporting packages. Your network should allow secure connections to remote hosts (like SSH). We will provide you with instructions to install the required software packages. Udacity does not provide any hardware.