Applied Computer Vision

Classification, detection and segmentation with modern backbones — data pipelines, augmentation, transfer learning and deployment.

⏱ 9 weeks🎚 Intermediate🧩 3 projects · Capstone🖥 Any device · our GPUs

Apply for this course Where this leads

Draft syllabus. Timeline and topics are indicative and being finalised with the teaching team — the shape is right, the week-by-week detail may shift.

🎯 Who it's for

Engineers with deep-learning basics who want to work on vision.

✅ Prerequisites

Deep Learning Foundations with PyTorch or equivalent
Python fluency
Comfort with the training loop

Topics covered

Image data pipelines and augmentation
Backbones and transfer learning
Object detection

Segmentation
Evaluation — mAP, IoU
Deployment and optimisation

Expected completion timeline

9 weeks part-time at 8–12 hours per week ≈ 2.1 months. Self-paced learners can go faster; the live cohort keeps this pace.

Wk 1–3

Classification and transfer learning

You produce: A classifier fine-tuned on a custom dataset.

Wk 4–6

Detection and segmentation

You produce: A detection or segmentation model with proper metrics.

Wk 7–9

Deploy and capstone

You produce: An optimised model served behind an API, with a write-up.

🧭 Where this fits

Core of the Computer Vision / NLP Engineer path. See the career & salary map for the full path, the expected pay by region, and the total cost.

💳 Delivery & fees

Available in all four delivery formats. Fees vary by format and are confirmed on your advisor call; instalments available. [Add the fee and the next cohort date here.]

Add this to your plan

Book a call and we'll place this course in a full path toward the role you want.

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