Applied Object Detection & Segmentation

Overview

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This specialization prepares you to build, evaluate, and deploy production-ready object detection and image segmentation systems. Across eight hands-on courses, you'll learn to create quality-controlled vision datasets, train and evaluate models using metrics like mAP, IoU, and Dice, and diagnose performance issues through slice-level analysis and error logging. You'll build real-time detection pipelines with YOLOv8 and DeepSORT, refine segmentation outputs using post-processing techniques like CRF smoothing, and optimize models for edge deployment with TensorFlow Lite. The program also covers deploying scalable inference workflows using Docker and AWS Lambda, calibrating confidence scores for trustworthy predictions, and communicating results to technical and non-technical stakeholders. By completion, you'll have the end-to-end skills to take computer vision models from notebooks to reliable, production-grade systems.

Syllabus

Course 1: Optimize Vision Datasets: Augment and Analyze
Course 2: Deploy & Evaluate Vision Models Effectively
Course 3: Optimize and Deploy Edge AI Models
Course 4: Calibrate and Serve Confident AI Predictions
Course 5: Annotate and Analyze Objects for Vision
Course 6: Build & Evaluate Real-Time Object Detectors
Course 7: Balance and Analyze Image Segmentation
Course 8: Refine Segmentation: Boost Your AI Vision

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In this course, you will learn how to improve computer vision performance by optimizing the dataset before model training begins. You will examine how dataset characteristics such as class distribution, image resolution, aspect ratio, channel statistics, blur, corruption, and deployment gaps shape the choices you make about model families and preprocessing pipelines. You will move from analysis to action by selecting practical strategies for resizing, normalization, deduplication, and transfer learning based on the data you actually have. You will also learn how to use image augmentation to increase dataset diversity, reduce overfitting, and improve generalization without collecting new labeled data. Through examples and applied activities, you will evaluate semantic validity, match augmentation techniques to real dataset gaps, and design training-only pipelines that reflect deployment conditions. By the end of the course, you will have a structured, repeatable approach to analyzing and augmenting vision datasets so you can build more robust and reliable computer vision systems.