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

Advanced
2 weeks
Last Updated June 27, 2024

Skills you'll learn:

Object detectionPyTorch

Prerequisites:

Object-oriented programming basicsIntermediate PythonNeural network basicsBasic probabilityDeep learning framework proficiency

Advanced

2 weeks

Last Updated June 27, 2024

Skills you'll learn:

Object detection • PyTorch • Image classification • Data augmentation

Prerequisites:

Object-oriented programming basics • Intermediate Python • Neural network basics

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

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.

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.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
Retain knowledge longer
Apply new skills immediately

Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.