Whether you're launching a career in data science, developing expertise in deep learning and AI, or preparing for your next opportunity, WQU Labs provide a flexible way to build practical, in-demand technical skills through hands-on projects.
These self-paced, project-based programs are designed as applied credentials, giving learners the opportunity to develop and demonstrate their skills through authentic project work using real-world datasets. Working in cloud-based virtual machines, learners tackle the kinds of challenges faced by data scientists and AI practitioners across industries - entirely free of cost.
Self-Paced (Est. 10-16 weeks)
10-15 Hours Per Week
Computer Vision Lab:
The Computer Vision Lab is a specialization for practitioners ready to apply deep learning expertise to real-world visual intelligence problems, from medical imaging and crop monitoring to surveillance and biometrics. Through 6 self-paced projects, you'll clean and transform visual data, train custom computer vision models, and apply advanced techniques like transfer learning, gaining end-to-end skills from data preparation to model deployment.
Prerequisites:
Intermediate-level Python skills
Ability to manipulate basic data structures like lists and dictionaries, and write definitions for functions and classes
Familiarity with essential machine learning concepts, e.g. supervised and unsupervised learning, overfitting and regularization, and training, validation, and test sets
Application Requirements
Above Suggested Prerequisites
Passing score on Admissions Assessment (70% or higher)
The Computer Vision Lab is an advanced specialization designed for learners and practitioners who have a foundation in deep learning and neural networks and are ready to apply their existing knowledge to complex, real-world problems involving image and video data.
Before you attempt the Admissions Assessment, we recommend that you use the following free resources to help you prepare:
Python at LearnPython.org: Learn the Basics.
Applied Data Science Lab: WQU’s own Applied Data Science Lab is free and always available. The Applied Data Science Lab teaches you the Python and Machine Learning skills needed to succeed in the Computer Vision Lab.
Deep Learning Fundamentals Lab: WQU’s free Deep Learning Fundamentals Lab equips you with the core deep learning skills needed to progress into AI specializations such as Computer Vision. It is highly recommended for anyone looking to build a strong foundation in deep learning before advancing to more specialized applications.
Linear Algebra from Khan Academy: study the mathematical foundation for key concepts in neural networks, data transformations, and optimization algorithms that power machine learning models.
College Algebra: A full course with companion python code on YouTube.
Mathematics for Machine Learning: A free eBook available online and as a PDF.
Practical Deep Learning for Coders: A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
If you fail the Admissions Assessment
You have a second (and final) attempt after a 7-day waiting period.
Applicants who do not pass the test on their 2nd attempt are able to reapply to the Lab following a waiting period of 6 months from the date of their 2nd attempt.
Important Warning: Creating multiple accounts to attempt the Admissions Assessment is a violation of the University’s Academic Integrity policy. Any user identified for doing so will be immediately terminated and will not have the opportunity to be considered for enrollment in the Lab program.
Next Steps After Passing the Admissions Assessment
Complete your Student Profile;
Sign the User Agreement;
Take the mandatory Orientation Course;
Register for Project 1.
Once you have completed your profile and signed the User Agreement, you will be automatically enrolled in a mandatory Orientation Course, which can be accessed via My Courses in the top navigation of the WQU Learning Platform.
From walking you through the Lab’s structure to helping you navigate our Learning Platform and virtual machines, the Orientation Course takes about 1 hour to complete and covers everything you need to know to set you up for success.
Upon successful completion of the Orientation Course, course registration will NOT occur automatically. Instead, you are responsible to register for each of your Projects by navigating to My Courses and "Register".
The Computer Vision Lab curriculum is delivered on virtual machines, enabling learners to code alongside video lectures and engage with peers and instructors via collaborative forums and live office hours.
Credentials Earned Upon Successful Completion
Verified Digital Badge and Certificate
Project Descriptions
The Computer Vision Lab comprises six end-to-end projects.
Each successful project completion unlocks the registration for the next.
Wildlife Conservation in Côte d'Ivoire
Project 1 • self-paced
In this project, learners examine a data science competition helping scientists track animals in a wildlife preserve. The goal is to take images from camera traps and classify which animal, if any, is present. To complete the competition, learners expand their machine learning skills by creating more powerful neural network models that can take images as inputs and classify them into one of multiple categories.
Crop Disease in Uganda
Project 2 • self-paced
Working with a dataset of crop disease images from Uganda, learners build and train a convolutional neural network to classify images into five categories. They explore how to improve the performance of a computer vision model by using pre-trained models and optimizing training with techniques like Callbacks.
Traffic Monitoring in Bangladesh
Project 3 • self-paced
Using traffic video feed data from Dhaka, Bangladesh, learners develop real-time object detection systems to identify and label vehicles, pedestrians, and other traffic elements. They work with pre-trained models and extend existing architectures to detect custom objects specific to urban traffic analysis, creating solutions that can monitor traffic flow and congestion patterns.
Celebrity Sightings in India
Project 4 • self-paced
In this project, learners perform face detection and recognition tasks by using a video of an interview with Indian Olympic boxer Mary Kom. They use a state-of-the-art pre-trained Multi-task Cascaded Convolutional Network (MTCNN) model together with Inception-ResNet model to perform face recognition. The goal is to use selected video frames of Mary Kom and her interviewer and create a face embedding for each of them. This allows learners to detect their faces on new images. Learners conclude the project by wrapping their code into a Flask app that allows a user to upload an image and perform face recognition.
Medical Data Generation in Spain
Project 5 • self-paced
Working with medical imaging data, learners explore using neural networks to generate new images such as X-rays and MRIs. They accomplish this using Generative Adversarial Network (GAN) systems, both by building custom architectures and leveraging pre-trained models. Learners also create a web app using Streamlit to allow users to interact with the GAN. Additionally, learners use Git and GitHub to track the app's code.
Social Media Marketing in the United States
Project 6 • self-paced
In this project, learners use Stable Diffusion to create images from text descriptions. They assemble the Stable Diffusion pipeline using several pre-trained neural networks, and learn how to fine-tune the networks to include new image information. With the goal of generating meme-worthy images, learners also create and deploy a Streamlit app to be a front-end to their fine-tuned Stable Diffusion model. This allows a non-technical marketing team to generate such images easily.
Learning Outcomes
Map Challenges and Tasks
Map real-world challenges to machine learning tasks.
Dataset Preparation
Assess datasets and prepare them for model training.
Neural Networks
Identify the core concepts behind neural networks, such as model components, optimizers, loss functions and performance metrics.
Model Building
Build, train, and evaluate deep neural networks for computer vision tasks.
Model Deployment
Deploy models and model output in AI.
Debugging
Select appropriate resources and strategies when debugging a project.
AI Ethics
Summarize the main ethical and environmental issues confronting deep learning, as well as model-building techniques that favor fairness and sustainability.
Community of Practice
Deconstruct underlying values, areas of focus, and professional concerns of data science practitioners.