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Andy Brown,
Andrew Paster,
Anthony Navarro,
Tarin Ziyaee,
Elecia White,
Cezanne Camacho,
and
Sebastian Thrun
In this course you’ll learn how a computer sees an image, and how we can use machine learning to teach a computer to identify images programmatically.
Register for this course and see more details by visiting:
OpenCourser.com/course/acgb1l/computer
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What's inside
Syllabus
Students will learn how to program an image classifier using computer vision techniques. Along the way you'll learn about machine learning, color transformation, feature extraction, and more!
Build a classification pipeline that takes in an image of a traffic and outputs a label that classifies the image as a: red, green, or yellow traffic light.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by experts in computer vision techniques, machine learning, and artificial intelligence
Helps learners develop skills in image classification, feature extraction, and machine learning
Develops foundational skills in computer vision, which are in high demand in various industries
Covers a comprehensive range of topics, including image processing, feature engineering, and supervised learning
Requires learners to have basic programming skills and some prior knowledge in mathematics and statistics
May require access to specialized hardware or software for optimal performance
Save this course
Save Computer Vision and Machine Learning to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Computer Vision and Machine Learning with these
activities:
Review image processing basics
Show steps
Start practicing image processing techniques to refresh your understanding and build a solid foundation for the course.
Browse courses on
Image Processing
Show steps
-
Review basic image processing operations such as cropping, resizing, and color adjustments.
-
Explore different image formats and their properties.
-
Understand the concept of image resolution and its impact on image quality.
Find a mentor in the field of computer vision
Show steps
To enhance your learning and gain insights from experts, connect with a mentor who has experience in computer vision.
Show steps
-
Network with professionals at industry events.
-
Reach out to professors or researchers in the field.
-
Join online communities and forums related to computer vision.
Review the basics of computer vision
Show steps
Revisit the fundamentals of computer vision to enhance your understanding of the course material.
Browse courses on
Computer Vision
Show steps
-
Read introductory articles or books on computer vision
-
Review online tutorials or videos on basic computer vision concepts
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Volunteer at a local robotics club
Show steps
To gain practical experience and apply your knowledge, volunteer at a local robotics club where you can work on projects involving image recognition.
Show steps
-
Find a local robotics club that aligns with your interests
-
Contact the club and inquire about volunteer opportunities
-
Attend club meetings and participate in projects related to image recognition.
Practice image classification using scikit-learn
Show steps
Engage in hands-on practice to strengthen your understanding of image classification techniques and their implementation using scikit-learn.
Show steps
-
Load and preprocess image datasets.
-
Train and evaluate image classifiers using different algorithms and parameters.
-
Analyze the performance of your classifiers and identify areas for improvement.
Attend a workshop on image recognition
Show steps
To expand your knowledge and learn from experts, attend a workshop specifically focused on image recognition.
Show steps
-
Research and identify relevant workshops
-
Register for the workshop and prepare any necessary materials
-
Attend the workshop and actively participate in discussions and activities.
Build a simple image classifier using OpenCV
Show steps
Gain practical experience in applying computer vision techniques by constructing a basic image classifier.
Browse courses on
OpenCV
Show steps
-
Find a tutorial or online course on building an image classifier with OpenCV
-
Follow the instructions in the tutorial to implement the classifier
-
Test the classifier on your own image dataset
Attend a workshop on advanced image processing techniques
Show steps
Expand your knowledge and learn from experts by attending a workshop focused on advanced image processing techniques.
Browse courses on
Image Processing
Show steps
-
Research and identify workshops relevant to your interests
-
Register for the workshop and attend the sessions
Attend an image classification workshop
Show steps
Immerse yourself in a learning environment dedicated to image classification, where you can interact with experts and share knowledge with peers.
Show steps
-
Research and identify relevant image classification workshops.
-
Register for a workshop that aligns with your learning goals.
-
Actively participate in the workshop, ask questions, and engage in discussions.
Practice image classification with Python
Show steps
To improve your understanding of how computers see and classify images, practice writing Python code to classify images.
Show steps
-
Find a dataset of images.
-
Write a Python script to load and preprocess the images.
-
Create a machine learning model to classify the images.
-
Evaluate the performance of your model.
Solve coding challenges related to image processing
Show steps
Enhance your problem-solving abilities and deepen your understanding of image processing concepts by tackling coding challenges.
Browse courses on
Image Processing
Show steps
-
Find online coding platforms or resources that offer image processing challenges
-
Attempt to solve the challenges on your own
-
Review solutions and learn from others' approaches
Follow tutorials on advanced computer vision techniques
Show steps
To enhance your understanding and learn about cutting-edge techniques, explore and follow tutorials that cover advanced computer vision topics.
Show steps
-
Identify reputable sources for computer vision tutorials
-
Select tutorials that align with your interests and skill level
-
Complete the tutorials and practice the concepts hands-on.
Contribute to open-source computer vision projects
Show steps
Gain experience in real-world computer vision applications and contribute to the open-source community.
Browse courses on
Open Source
Show steps
-
Find open-source computer vision projects that align with your interests
-
Identify areas where you can contribute, such as bug fixing or feature development
-
Read the project's documentation and contribute according to their guidelines
Build a portfolio of image classification projects
Show steps
Showcase your skills by creating a portfolio of image classification projects that demonstrate your proficiency in applying the concepts learned in the course.
Show steps
-
Identify real-world applications of image classification.
-
Develop and implement image classification solutions for these applications.
-
Document and present your projects to highlight your understanding and abilities.
Build a traffic light classifier
Show steps
To solidify your understanding of image classification, build a traffic light classifier that can identify red, yellow, and green lights.
Show steps
-
Collect a dataset of traffic light images.
-
Preprocess the images and extract features.
-
Train a machine learning model to classify the images.
-
Deploy the model and test its accuracy.
Review image processing basics
Show steps
Start practicing image processing techniques to refresh your understanding and build a solid foundation for the course.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Review basic image processing operations such as cropping, resizing, and color adjustments.
- Explore different image formats and their properties.
- Understand the concept of image resolution and its impact on image quality.
Find a mentor in the field of computer vision
Show steps
To enhance your learning and gain insights from experts, connect with a mentor who has experience in computer vision.
Show steps
Show steps
Show steps
- Network with professionals at industry events.
- Reach out to professors or researchers in the field.
- Join online communities and forums related to computer vision.
Review the basics of computer vision
Show steps
Revisit the fundamentals of computer vision to enhance your understanding of the course material.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Read introductory articles or books on computer vision
- Review online tutorials or videos on basic computer vision concepts
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Volunteer at a local robotics club
Show steps
To gain practical experience and apply your knowledge, volunteer at a local robotics club where you can work on projects involving image recognition.
Show steps
Show steps
Show steps
- Find a local robotics club that aligns with your interests
- Contact the club and inquire about volunteer opportunities
- Attend club meetings and participate in projects related to image recognition.
Practice image classification using scikit-learn
Show steps
Engage in hands-on practice to strengthen your understanding of image classification techniques and their implementation using scikit-learn.
Show steps
Show steps
Show steps
- Load and preprocess image datasets.
- Train and evaluate image classifiers using different algorithms and parameters.
- Analyze the performance of your classifiers and identify areas for improvement.
Attend a workshop on image recognition
Show steps
To expand your knowledge and learn from experts, attend a workshop specifically focused on image recognition.
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register for the workshop and prepare any necessary materials
- Attend the workshop and actively participate in discussions and activities.
Build a simple image classifier using OpenCV
Show steps
Gain practical experience in applying computer vision techniques by constructing a basic image classifier.
Browse courses on
OpenCV
Show steps
Show steps
Show steps
- Find a tutorial or online course on building an image classifier with OpenCV
- Follow the instructions in the tutorial to implement the classifier
- Test the classifier on your own image dataset
Attend a workshop on advanced image processing techniques
Show steps
Expand your knowledge and learn from experts by attending a workshop focused on advanced image processing techniques.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Research and identify workshops relevant to your interests
- Register for the workshop and attend the sessions
Attend an image classification workshop
Show steps
Immerse yourself in a learning environment dedicated to image classification, where you can interact with experts and share knowledge with peers.
Show steps
Show steps
Show steps
- Research and identify relevant image classification workshops.
- Register for a workshop that aligns with your learning goals.
- Actively participate in the workshop, ask questions, and engage in discussions.
Practice image classification with Python
Show steps
To improve your understanding of how computers see and classify images, practice writing Python code to classify images.
Show steps
Show steps
Show steps
- Find a dataset of images.
- Write a Python script to load and preprocess the images.
- Create a machine learning model to classify the images.
- Evaluate the performance of your model.
Solve coding challenges related to image processing
Show steps
Enhance your problem-solving abilities and deepen your understanding of image processing concepts by tackling coding challenges.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Find online coding platforms or resources that offer image processing challenges
- Attempt to solve the challenges on your own
- Review solutions and learn from others' approaches
Follow tutorials on advanced computer vision techniques
Show steps
To enhance your understanding and learn about cutting-edge techniques, explore and follow tutorials that cover advanced computer vision topics.
Show steps
Show steps
Show steps
- Identify reputable sources for computer vision tutorials
- Select tutorials that align with your interests and skill level
- Complete the tutorials and practice the concepts hands-on.
Contribute to open-source computer vision projects
Show steps
Gain experience in real-world computer vision applications and contribute to the open-source community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find open-source computer vision projects that align with your interests
- Identify areas where you can contribute, such as bug fixing or feature development
- Read the project's documentation and contribute according to their guidelines
Build a portfolio of image classification projects
Show steps
Showcase your skills by creating a portfolio of image classification projects that demonstrate your proficiency in applying the concepts learned in the course.
Show steps
Show steps
Show steps
- Identify real-world applications of image classification.
- Develop and implement image classification solutions for these applications.
- Document and present your projects to highlight your understanding and abilities.
Build a traffic light classifier
Show steps
To solidify your understanding of image classification, build a traffic light classifier that can identify red, yellow, and green lights.
Show steps
Show steps
Show steps
- Collect a dataset of traffic light images.
- Preprocess the images and extract features.
- Train a machine learning model to classify the images.
- Deploy the model and test its accuracy.
Career center
Learners who complete Computer Vision and Machine Learning will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
This course on Computer Vision and Machine Learning serves as a great foundation for Computer Vision Engineers. From training image classifiers to using computer vision techniques to analyze images, this course will go over many of the necessary skills for success as a Computer Vision Engineer.
Machine Learning Engineer
Machine Learning Engineer
Computer Vision plays an essential role in Machine Learning and is widely used across many different career fields. This would be a valuable course for any Machine Learning Engineer who wants to work with Computer Vision and make the most out of the tools available to them. This course can help you develop the foundational knowledge necessary to work with Computer Vision and will help build a foundation for further study
Quantitative Analyst
Quantitative Analyst
This course can provide Quantitative Analysts with a great foundation in Computer Vision. The course goes over how to train image classifiers, which can be of great help when analyzing images for trading and risk management.
Data Scientist
Data Scientist
If you want to work with both Machine Learning and Computer Vision as a Data Scientist, then this course may be of some help to you. It will aid in your ability to use images to solve business problems or develop cutting-edge applications. It will also go over feature extraction, which would be helpful for extracting insights from data for analysis and visualization.
Security Analyst
Security Analyst
Computer Vision is used for many security applications, such as facial recognition and object detection. This course can give Security Analysts a great overview of what Computer Vision can do and how to use its capabilities in their own work.
Digital Marketing Manager
Digital Marketing Manager
Computer Vision is becoming more common in Digital Marketing and can be used to analyze images of products and faces, generate images, and perform image classification. This course will give Digital Marketing Managers the ability to understand how Computer Vision can be used to help optimize marketing efforts.
Data Analyst
Data Analyst
As a Data Analyst, you can leverage Computer Vision to extract meaningful insights and solutions from images. This course will introduce you to many of the core concepts of Computer Vision and can help Data Analysts gain an advantage in their work.
Technical Writer
Technical Writer
Technical Writers who want to write about Computer Vision or who want to work with Computer Vision teams can benefit from this course. It will provide the foundational knowledge necessary to accurately write about the subject and its latest trends.
Software Engineer
Software Engineer
This course could be helpful for Software Engineers who want to work with or develop Computer Vision applications. There are various opportunities for Software Engineers who want to work in Computer Vision, and this course can help you build a foundation from which to grow.
Operations Research Analyst
Operations Research Analyst
Computer Vision can be used to help solve complex operations research problems in various industries. This course will introduce Operations Research Analysts to Computer Vision and its capabilities, which they can use to improve problem-solving abilities.
Business Analyst
Business Analyst
Business Analysts can benefit from knowledge of Computer Vision and how it can be used to solve business problems. This course will provide a foundation in Computer Vision for Business Analysts and help them contribute to initiatives and projects that leverage Computer Vision.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
This course could be useful for those interested in moving into a career as an Artificial Intelligence Engineer. It will go over using computer vision techniques with machine learning, which are both very valuable skills for this role. Additionally, it will introduce color transformation and feature extraction, which can be added to your skill set.
User Experience Designer
User Experience Designer
With Computer Vision becoming more popular, it is also important for User Experience Designers to gain a basic understanding of how it works and its capabilities. This course will help User Experience Designers see how Computer Vision can be utilized in design and can help inform them when making experience decisions.
Product Manager
Product Manager
Computer Vision has become an invaluable skill for Product Managers who want to bring products to market that use cutting-edge image analysis and computer vision technology. This course covers the key underlying principles of Computer Vision and can allow you to make more informed decisions when managing products that utilize it.
Robotics Engineer
Robotics Engineer
Computer Vision is commonly used in Robotics to help Robots perceive the world around them. While this course does not delve into the engineering side of Robotics, it will give those interested in the field a solid foundation to build from if they want to work with the computer vision side of Robotics.
For more career information including salaries, visit:
OpenCourser.com/course/acgb1l/computer
Reading list
We've selected five books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Computer Vision and Machine Learning.
Save
This textbook from renowned researcher Richard Szeliski provides a solid foundation in computer vision algorithms and how to use them in practical applications. will be especially helpful with the course's coverage of machine learning and feature extraction techniques for computer vision.
This textbook from Jason Brownlee covers the fundamentals of deep learning for computer vision. It provides a hands-on approach to learning and using deep learning techniques for a variety of computer vision tasks.
From Jan Erik Solem practical guide to programming computer vision with Python. It covers a variety of computer vision tasks in Python, including image processing, feature extraction, and object detection. The book also provides hands-on examples of how to use Python for a variety of computer vision applications.
Save
From Steven LaValle practical guide to computer vision for augmented reality. It covers the latest techniques in computer vision for augmented reality and provides hands-on examples of how to use these techniques for augmented reality applications.
Save
From Jason Jerald practical guide to computer vision for virtual reality. It covers the latest techniques in computer vision for virtual reality and provides hands-on examples of how to use these techniques for virtual reality applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/acgb1l/computer
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Time
40320 hours
Level
Intermediate
Via
Udacity
Instructors
Andy Brown
Andrew Paster
Anthony Navarro
Tarin Ziyaee
Elecia White
Cezanne Camacho
Sebastian Thrun
Language
English
Good to know
Taught by experts in computer vision techniques, machine learning, and artificial intelligence
Helps learners develop skills in image classification, feature extraction, and machine learning
Develops foundational skills in computer vision, which are in high demand in various industries
Covers a comprehensive range of topics, including image processing, feature engineering, and supervised learning
Requires learners to have basic programming skills and some prior knowledge in mathematics and statistics
May require access to specialized hardware or software for optimal performance
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