Computer Vision Applications
This course provides an introduction to machine learning for computer vision with a focus on practical applications relevant to industry teams. In this course, we will “reverse-engineer” a number of applications, such as traffic flow analysis, digital medicine, optical character recognition, and video analytics. We will discuss the fundamental machine learning principles required to build these applications, focusing on practical tools instead of algorithmic details. You will build these applications from scratch, using open-source tools that cover the full stack of modern machine learning, from datasets to deployment. By the end of the course, you will have built a portfolio of computer vision applications that you can reference or share with your team and colleagues.
Course taught by expert instructors
Abubakar Abid
Machine Learning Team Lead at Hugging Face
Abubakar has been building machine learning models for over a decade. He did his PhD at Stanford in deep learning applied to medical images and videos. During his PhD, he developed Gradio (www.gradio.dev), an open-source Python library for creating GUIs for machine learning models. Since Gradio’s acquisition by Hugging Face, Abubakar continues to lead the Gradio team and also teaches machine learning at Hugging Face and beyond!
The course
Learn and apply skills with real-world projects.
Software engineers who want to build vision applications for prototyping or deployment without worrying too much about the underlying algorithmic details.
Machine learning engineers who may already know the algorithms but are interested in building practical computer vision applications using the best open-source tools.
Ability to write Python proficiently and work with documented libraries
Experience using Jupyter notebooks or Google Colab notebooks recommended
Basic understanding of machine learning (no experience in computer vision is required)
Try these prep courses first
- Learn
- Steps to do machine learning: from building datasets to deploying applications
- Overview of algorithms for image classification, including an overview of recent progress in deep deep learning in the last decade (from AlexNet to Transformers)
- Training vs. fine-tuning machine learning models
- How to download models from the Hugging Face Hub using the transformers library
- How to finetune a model for image classification
You will build a machine learning model to classify plants, and deploy it as an application using the concepts we have studied our first week. You will then be able to take your phone and test the web application.
- Learn
- The machine learning system that powers a self-driving car
- Different kinds of image segmentation (semantic segmentation, object detection)
- Overview of algorithms for image segmentation
- How to download datasets from the Hugging Face Hub using the datasets library
- How to train an image segmentation model from scratch
You will build a machine learning model to segment images for self-driving cars (e.g. into pedestrians, roads, etc.), and deploy it as an application using the concepts we have studied our second week. You will then be able to take your application and test it with real images of the road.
- Learn
- Machine learning system that powers the FaceID authentication system for Apple iPhones
- Different ways images can be converted into embeddings
- Different uses of embeddings
- How to download datasets from the Hugging Face Hub using the datasets library
- How to train an image segmentation model from scratch
You will build a machine learning model that can recognize if a photo is of an authorized person. You will be able to deploy it as an application using the concepts we have studied our third week. You will be able to test it with pictures from a webcam / phone camera.
- Learn
- Machine learning models for generating images including GANs and diffusion models
- Different uses of image generation
- Ethical risks and biases that are part of such applications
- How to train an image generation model from scratch
- How to add class conditioning so that you can generate specific kinds of images
You will build a machine learning model that can generate new images of people that do not exist. You will then deploy it on a web application and use it to generate new images.
A course you'll actually complete. AI-powered learning that drives results.
AI-powered learning
Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.
Live courses by leading experts
Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.
Practice on the cutting edge
Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.
Flexible schedule for busy professionals
We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.
Completion certificates
Each course comes with a certificate for learners to add to their resume.
Best-in-class outcomes
15-20x engagement compared to async courses
Support & accountability
You are never alone, we provide support throughout the course.
Get reimbursed by your company
More than half of learners get their Courses and Memberships reimbursed by their company.
Hundreds of companies have dedicated L&D and education budgets that have covered the costs.
Course success stories
Learn together and share experiences with other industry professionals
The concepts and tools covered are highly relevant to industrial applications of computer vision, the projects are fun and help to solidify learnings, and the quality of instruction is very high.
I entered this course with a basic knowledge of the fundamentals of machine learning, and by the time I finished I felt confident enough to start tackling computer vision projects on my own. This was a great crash course in both computer vision as well as modern deep learning workflows.
Abubakar is amongst the best teachers I’ve ever had. I was entirely new to machine learning yet he was able to distill complicated concepts clearly and effectively. I felt everywhere else was teaching me just the surface, but Abubakar was able to tie the theory, practice and intuition together.
The course was amazing. The content was great, the projects were not only hands on, but also practical applications that I could send to my friends. And also this course set the foundations for me to develop other projects. I also liked a lot that we used huggingface and other pretty new technologies that are not normally part of other ML online courses that I've seen.
This course is a perfect course for one yearning to build AI vision applications. I had lot of fun learning and working through the project in this course!
It has been my pleasure to be Abubakar Abid's student as he has taught visual algorithms. Abubakar is an excellent educator with a great ability to explain complex concepts in a simple and intuitive manner, something that has made learning enjoyable for me. Not only is he very highly capable from the technical aspects, but he is also an outstanding communicator
