Nanodegree Program
Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models.
Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models.
Object tracking • Slam • Recurrent neural networks • Object detection
Object-oriented programming basics • Object-oriented Python • Intermediate Python
Courses In This Program
Course 1 • 45 minutes
Welcome to the Nanodegree Program!
Welcome to Udacity! We're excited to share more about your Nanodegree program and start this journey with you!
Lesson 1
Welcome!
Welcome to Udacity. Takes 5 minutes to get familiar with Udacity courses and gain some tips to succeed in courses.
Lesson 2
Getting Help
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
Lesson 1
Welcome!
Welcome to Udacity. Takes 5 minutes to get familiar with Udacity courses and gain some tips to succeed in courses.
Lesson 2
Getting Help
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
Course 2 • 4 weeks
Introduction to Computer Vision
Master computer vision and image processing essentials. Learn to extract important features from imagedata, and apply deep learning techniques to classification tasks
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.
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.
Course 3 • 40 minutes
Optional: Cloud Computing
Lesson 1
Optional: Cloud Computing with AWS
Take advantage of Amazon's GPUs to train your neural network faster. In this lesson, you'll learn how to setup an instance on AWS and train a neural network on a GPU.
Lesson 1
Optional: Cloud Computing with AWS
Take advantage of Amazon's GPUs to train your neural network faster. In this lesson, you'll learn how to setup an instance on AWS and train a neural network on a GPU.
Course 4 • 1 month
Advanced 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.
Lesson 1
Advanced CNN Architectures
Learn about advances in CNN architectures and see how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images.
Lesson 2
YOLO
Learn about the YOLO (You Only Look Once) multi-object detection model and work with a YOLO implementation.
Lesson 3
RNN's
Explore how memory can be incorporated into a deep learning model using recurrent neural networks (RNNs). Learn how RNNs can learn from and generate ordered sequences of data.
Lesson 4
Long Short-Term Memory Networks (LSTMs)
Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Lesson 5
Hyperparameters
Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.
Lesson 6
Optional: Attention Mechanisms
Attention is one of the most important recent innovations in deep learning. In this section, you'll learn how attention models work and go over a basic code implementation.
Lesson 7
Image Captioning
Learn how to combine CNNs and RNNs to build a complex, automatic image captioning model.
Lesson 8 • Project
Project: Image Captioning
Train a CNN-RNN model to predict captions for a given image. Your main task will be to implement an effective RNN decoder for a CNN encoder.
Lesson 1
Advanced CNN Architectures
Learn about advances in CNN architectures and see how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images.
Lesson 2
YOLO
Learn about the YOLO (You Only Look Once) multi-object detection model and work with a YOLO implementation.
Lesson 3
RNN's
Explore how memory can be incorporated into a deep learning model using recurrent neural networks (RNNs). Learn how RNNs can learn from and generate ordered sequences of data.
Lesson 4
Long Short-Term Memory Networks (LSTMs)
Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Lesson 5
Hyperparameters
Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.
Lesson 6
Optional: Attention Mechanisms
Attention is one of the most important recent innovations in deep learning. In this section, you'll learn how attention models work and go over a basic code implementation.
Lesson 7
Image Captioning
Learn how to combine CNNs and RNNs to build a complex, automatic image captioning model.
Lesson 8 • Project
Project: Image Captioning
Train a CNN-RNN model to predict captions for a given image. Your main task will be to implement an effective RNN decoder for a CNN encoder.
Taught By The Best
Sebastian Thrun
Founder and Executive Chairman, Udacity
As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. 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.
Jay Alammar
Instructor
Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.
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.
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.
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.
Ratings & Reviews
Average Rating: 4.7 Stars
450 Reviews
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Month-To-Month
4 Months
Average time to complete a Nanodegree program
*Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.
Advanced Computer Vision and Deep Learning
1 month
, Advanced
Deep Learning
(909)
4 months
, Intermediate
Deep Reinforcement Learning
(328)
2 months
, Advanced
Introduction to Computer Vision
4 weeks
, Advanced
Object Tracking and Localization
4 weeks
, Advanced
Natural Language Processing
(275)
2 months
, Advanced
Introduction to Machine Learning with TensorFlow
(256)
2 months
, Intermediate
Introduction to Machine Learning with Pytorch
(235)
3 months
, Intermediate
AI for Trading
(496)
5 months
, Advanced
Introduction to Self-Driving Cars
(363)
3 months
, Intermediate
Convolutional Neural Networks
4 weeks
, Intermediate
Computer Vision and Generative AI
4 weeks
, Intermediate
Generative AI
4 months
, Intermediate
Computing With Natural Language
4 weeks
, Advanced
Machine Learning DevOps Engineer
(87)
4 months
, Advanced
Deep Learning Topics with Computer Vision and NLP
4 weeks
, Intermediate
Your subscription also includes:
Your subscription also includes:
Advanced Computer Vision and Deep Learning
1 month
, Advanced
Deep Learning
(909)
4 months
, Intermediate
Deep Reinforcement Learning
(328)
2 months
, Advanced
Introduction to Computer Vision
4 weeks
, Advanced
Object Tracking and Localization
4 weeks
, Advanced
Natural Language Processing
(275)
2 months
, Advanced
Introduction to Machine Learning with TensorFlow
(256)
2 months
, Intermediate
Introduction to Machine Learning with Pytorch
(235)
3 months
, Intermediate
AI for Trading
(496)
5 months
, Advanced
Introduction to Self-Driving Cars
(363)
3 months
, Intermediate
Convolutional Neural Networks
4 weeks
, Intermediate
Computer Vision and Generative AI
4 weeks
, Intermediate
Generative AI
4 months
, Intermediate
Computing With Natural Language
4 weeks
, Advanced
Machine Learning DevOps Engineer
(87)
4 months
, Advanced
Deep Learning Topics with Computer Vision and NLP
4 weeks
, Intermediate
Get Started Today
Computer Vision
Month-To-Month
- Unlimited access to our top-rated courses
- Real-world projects
- Personalized project reviews
- Program certificates
- Proven career outcomes
4 Months
Average time to complete a Nanodegree program
- All the same great benefits in our month-to-month plan
- Most cost-effective way to acquire a new set of skills
Advanced Computer Vision and Deep Learning
1 month
, Advanced
Deep Learning
(909)
4 months
, Intermediate
Deep Reinforcement Learning
(328)
2 months
, Advanced
Introduction to Computer Vision
4 weeks
, Advanced
Object Tracking and Localization
4 weeks
, Advanced
Natural Language Processing
(275)
2 months
, Advanced
Introduction to Machine Learning with TensorFlow
(256)
2 months
, Intermediate
Introduction to Machine Learning with Pytorch
(235)
3 months
, Intermediate
AI for Trading
(496)
5 months
, Advanced
Introduction to Self-Driving Cars
(363)
3 months
, Intermediate
Convolutional Neural Networks
4 weeks
, Intermediate
Computer Vision and Generative AI
4 weeks
, Intermediate
Generative AI
4 months
, Intermediate
Computing With Natural Language
4 weeks
, Advanced
Machine Learning DevOps Engineer
(87)
4 months
, Advanced
Deep Learning Topics with Computer Vision and NLP
4 weeks
, Intermediate
Related Programs
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Advanced Computer Vision and Deep Learning
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Object Tracking and Localization
4 weeks
, Advanced
Natural Language Processing
(275)
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Introduction to Machine Learning with TensorFlow
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Introduction to Machine Learning with Pytorch
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AI for Trading
(496)
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, Advanced
Introduction to Self-Driving Cars
(363)
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Convolutional Neural Networks
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, Intermediate
Computer Vision and Generative AI
4 weeks
, Intermediate
Generative AI
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, Intermediate
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4 weeks
, Advanced
Machine Learning DevOps Engineer
(87)
4 months
, Advanced
Deep Learning Topics with Computer Vision and NLP
4 weeks
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