Applied AI Lab: Deep Learning for Computer Vision
Learn How to ApplyAdvance Your AI skills: Develop Deep Learning Models for Computer Vision
- Completely Online
- 100% Free of Cost
- Rigorous Focus on Applied Learning
Computer Vision as a Gateway to Deep Learning
Computer vision stands out as one of the most accessible and impactful applications of deep learning with its use of neural networks to interpret complex visual data. Its ability to address real-world problems makes it the ideal starting point for those ready to master artificial intelligence in an applied setting. Beyond its use in specialized roles such as computer vision engineering, AI research, and robotics development, these skills are increasingly valuable in healthcare, where medical imaging aids diagnostics; agriculture, for monitoring crop health; and security, with applications in surveillance and biometrics.
Our self-paced Applied AI Lab focuses on practical applications, using computer vision as a hands-on framework for building essential deep learning skills. Through 6 real-world projects, you’ll learn to clean and transform visual data, train custom computer vision models, and apply advanced techniques like transfer learning. By the end of the program, you’ll will be equipped with end-to-end computer vision skills, from data preparation to model deployment, ready to tackle complex challenges across industries.
Applied AI Lab: Deep Learning for Computer Vision
Rolling Admissions
Upon Acceptance
Entirely Free
10-16 weeks
- 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
- Including supervised and unsupervised learning, overfitting and regularization, and training, validation, and test sets
- Passing score on Admissions Quiz (66% or higher)
Self-paced, 10-15h per week
Sharable Credly Certification
"Mastering deep learning for computer vision empowers young professionals with practical tools to solve real-world challenges across industries, from healthcare to agriculture, positioning them to lead with expertise in ethical, sustainable AI, and to tackle complex, meaningful problems."
What You'll Learn
The Applied AI Lab curriculum is delivered on virtual machines, enabling students to code alongside video lectures and engage with peers and instructors via collaborative forums and live office hours. After successfully completing the Lab, students earn an easily shareable WQU badge issued by Credly.
Lab Outcomes
Mapping Challenges to 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
Frequently Asked Questions
How does the Applied AI Lab work?
The Applied AI Lab is structured around six hands-on projects, each to be completed in sequence. These projects address real-world challenges, such as wildlife conservation, crop disease monitoring, and traffic flow analysis, allowing students to apply their skills in impactful, practical contexts.
The Lab is self-paced, so there’s no fixed deadline to complete it. Most students finish within 100-150 hours. All project work is completed on virtual machines, enabling students to code alongside video lectures and engage with peers in collaborative forums.
How can I prepare for the Applied AI Lab Admissions Quiz?
The Applied AI Lab is an advanced learning opportunity designed to help you master the core concepts behind neural networks through six hands-on projects ranging from image classification to generative AI. Applicants are expected to have the following prerequisite skills:
- Intermediate-level Python programming
- Ability to manipulate basic data structures like lists and dictionaries, and write definitions for functions and classes
- Familiarity with essential machine learning concepts, including supervised and unsupervised learning, overfitting and regularization, and training, validation, and test sets
All applicants must pass an Admissions Quiz with a minimum passing score of 66%. Before you attempt the Admissions Quiz, 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 Applied AI Lab.
- 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.
What happens if I fail the Admissions Quiz?
If you fail the Admissions Quiz for the Applied AI Lab, you’ll have a second chance to retake it after a 7-day waiting period. If you do not pass the Quiz on the second attempt, you may reapply to the Lab after a 6-month waiting period.
Please note that the Lab is intended for learners with these prerequisite skills:
- Intermediate-level Python programming
- Ability to manipulate basic data structures like lists and dictionaries, and write definitions for functions and classes
- Familiarity with essential machine learning concepts, including supervised and unsupervised learning, overfitting and regularization, and training, validation, and test sets