Computer Vision & Deep Learning

Description

Image classification, detection and segmentation — from data collection through training to a model that holds up outside the lab.

Applied computer vision for engineers. It covers the main task types and when each fits, building an image dataset that doesn’t sabotage you, transfer learning as the default starting point, evaluation that reflects real conditions, and deploying vision models where latency and hardware actually constrain you.

Who this is for

• Engineers with some ML background moving into vision
• Teams evaluating whether a vision approach is feasible for a real problem
• Developers integrating vision into an existing product
• Anyone whose model scores well on test data and fails in the field

What you’ll be able to do

• Choose correctly between classification, detection and segmentation
• Build and label an image dataset that reflects real deployment conditions
• Use transfer learning effectively rather than training from scratch
• Apply augmentation that helps instead of adding noise
• Evaluate with the metrics each task type actually requires
• Deploy within real latency and hardware constraints

Syllabus — 5 modules, 39 study hours

1. Task types and problem fit (6 hrs)
2. Data and labelling (9 hrs)
3. Transfer learning and training (9 hrs)
4. Evaluation for vision (7 hrs)
5. Deployment and constraints (8 hrs)

Includes a 7-week study plan, practice tasks with stated learning outcomes, an honest readiness self-assessment and a progress tracker.

Independent study material. Completing it does not award a certification or credential.