At 10-15 hrs/week
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This program requires experience with Python, statistics, machine learning, and deep learning.See detailed requirements.
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 Detection
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 Captioning
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
from industry experts
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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 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 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 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 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 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 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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Career-seeking and job-ready graduates found a new, better job within six months of graduation.
Average salary increase for graduates who found a new, better job within six months of graduation.
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.
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.
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.
No. This Nanodegree program accepts all applicants regardless of experience and specific background.
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:
Basic shell scripting:
Basic statistical knowledge, including:
Intermediate differential calculus and linear algebra, including:
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:
The Computer Vision Nanodegree program is comprised of content and curriculum to support three (3) projects. Once you subscribe to a Nanodegree program, you will have access to the content and services for the length of time specified by your subscription. 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.
Please see the Udacity Nanodegree program FAQs found here for policies on enrollment in our programs.
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.
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.
That's a great Nanodegree.
I thoroughly enjoyed completing the exercises in parallel to the lecture videos, which is unique to the Udacity style of learning.
One of the most incredible NDs ever! I've being learning more than a regular graduation. I am full of perspectives.
Very practical, I learnt a lot from this experience.
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.