Machine learning & data science
Build image models (detection/classification)
OpenCV + pretrained CNN/YOLO fine-tuning for a practical vision task.
~20 focused hours·intermediate
Tools: OpenCV, PyTorch/torchvision, YOLO (Ultralytics), Roboflow/LabelImg
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Preprocess images with OpenCV (resize, normalize, augment, color space conversions)
- Fine-tune a pretrained CNN (ResNet/EfficientNet) for an image classification task
- Fine-tune or run inference with a detection model (YOLO) for object detection
- Prepare and validate an annotated image dataset (bounding boxes) in the right format
- Evaluate detection/classification with the right metrics (mAP, IoU, precision/recall)
- Handle real-world image quality issues (lighting, blur, occlusion, low resolution)
- Optimize a vision model for reasonable inference latency, not just accuracy
Needs first: Build and train neural networks in PyTorch
Learn — free, link-checked
The few resources that matter
Read · intermediate · 60 min · docs.ultralytics.com
YOLO Object Detection & Segmentation
The docs for the most-used open detection model in Indian CV job postings -- train and evaluate a custom detector with a few lines of code. — Ultralytics
Read · intermediate · 150 min · docs.opencv.org
OpenCV-Python Tutorials
Official image-processing fundamentals (thresholding, contours, feature detection) that every CV pipeline needs before a model even runs. — OpenCV
Course · intermediate · 180 min · kaggle.com
Computer Vision
Notebook-based course on CNNs, transfer learning and data augmentation with an immediate Kaggle submission to prove the result. — Kaggle Learn
Course · intermediate · 1200 min · course.fast.ai
Practical Deep Learning for Coders
Top-down, code-first course (free videos + notebooks) that gets you to a fine-tuned, deployed image classifier before it explains the underlying math. — fast.ai
Practice
Indian vehicle/license-plate style object detector
Build an object detector (fine-tune YOLO via Ultralytics) to detect and localize a practical Indian-context object class, such as vehicle types (auto-rickshaw, two-wheeler, car) or license plates, using a small annotated dataset you label yourself with Roboflow/LabelImg or a public dataset. Evaluate with mAP and test on a handful of real photos you take yourself.
Done when
- Annotated dataset (100+ labeled images minimum) in YOLO format with a documented class list
- YOLO model fine-tuned and evaluated with mAP@0.5 reported on a held-out validation split
- Inference demonstrated on at least 5 real-world test images not in the training set, with visualized bounding boxes
- README documents failure cases observed (e.g. poor lighting, occlusion) and what would improve them
Prove it
Evidence a recruiter can check
- Public GitHub repo with dataset annotation format, training config and mAP results
- Before/after example images with predicted bounding boxes checked into the README
- Exported model weights (or Hugging Face Hub upload) others can run inference with
- Can explain live the mAP metric and a specific failure case observed
Interview
Questions you'll get asked
- Walk me through fine-tuning a pretrained model for a new image classification task
- How is object detection different from classification, and what does mAP measure?
- How would you build a system to detect defective products on a manufacturing line from images?
- What data augmentation would you use for a small image dataset, and why?
- How do you handle class imbalance in an object detection dataset (rare object classes)?
- What's IoU, and how does it factor into evaluating a detection model?
- How would you reduce inference latency for a vision model running on limited hardware?