Applied Object Detection & Segmentation

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.

This short course shows you how to build reliable vision datasets and configure detection models with confidence. You’ll learn how to run a quality-controlled annotation process, review bounding boxes, coach annotators, and check dataset consistency using IoU-based audits. You’ll also explore how to analyze object sizes with clustering to generate anchor box parameters for models like YOLOv8. Through compact videos, guided readings, and hands-on exercises, you’ll practice using tools such as CVAT and Python notebooks to complete tasks common in production vision teams. By the end, you’ll be able to create a clean bounding-box dataset and use real measurements to tune model anchors—skills that support robust, scalable computer-vision pipelines.

This short course helps you improve segmentation models when classes are heavily imbalanced and predictions show recurring errors. You will learn how to apply class-balancing strategies such as focal-dice hybrid loss and sampling adjustments on medical or industrial datasets where foreground pixels may be extremely rare. You will also learn how to analyze predicted masks using region measurements to spot over-segmentation, under-segmentation, and shape-specific failures. Through concise videos, hands-on activities, and reflective checkpoints with Coach, you will practice improving recall, inspecting connected components, and building simple error logs that uncover patterns. By the end, you will have a repeatable approach for balancing datasets and diagnosing mask-level errors in production-ready segmentation workflows.

Build & Evaluate Real-Time Object Detectors is an intermediate hands-on course for ML engineers who need to deploy fast, accurate object detectors under real-world constraints. When accuracy falls short of KPIs, or FPS drops below target, you need the skills to diagnose metrics, recommend improvements, and evaluate whether a real-time pipeline meets requirements. You'll learn how to compute and interpret detection metrics like mAP and APsmall, identify causes of underperformance, and propose targeted improvements. Then you'll analyze a complete real-time detection pipeline using models like YOLOv8 and trackers like DeepSORT, and evaluate it against throughput requirements such as 25 FPS at 720p. Through short videos, practical readings, analysis-based labs, and a final graded assessment, you will develop the skills to evaluate detectors, recommend optimizations, and assess whether solutions meet real-time demands.

Building trustworthy AI requires more than accurate predictions—it requires confidence scores that genuinely reflect reality. In this short, hands-on course, you will learn how to evaluate and improve model calibration, apply temperature scaling to produce reliable confidence estimates, and deploy a scalable batch-inference pipeline using AWS Lambda. Through practical exercises, you will compute calibration metrics, visualize reliability diagrams, and integrate calibrated predictions into a serverless architecture that automatically processes incoming data and stores results for analytics. By the end of the course, you will be able to design inference workflows that are reproducible, auditable, and ready for real-world decision-making. These skills help bridge the gap between model development and production deployment, enabling you to deliver AI systems that teams can understand, trust, and use confidently.

In this hands-on course, you’ll learn how to move computer vision models from notebooks to the real world. You’ll build an end-to-end inference pipeline, package it into a reproducible API, and evaluate its performance using precision, recall, and mean Average Precision (mAP). You’ll also practice diagnosing errors, segmenting results by condition, and communicating insights like a professional MLOps engineer. By the end, you’ll be ready to deploy, evaluate, and iteratively improve vision models that teams can trust.

This course teaches you how to evaluate and optimize machine learning models for reliable performance on edge devices. You’ll learn how to move beyond overall accuracy by analyzing model behavior across meaningful data slices—such as device type or environmental conditions—to uncover hidden robustness and fairness issues. You’ll also explore how models are optimized for edge deployment using TensorFlow Lite, including how quantization affects model size, inference speed, and accuracy. Through videos, hands-on activities, and guided reflection, you’ll practice interpreting these trade-offs and communicating deployment readiness clearly. By the end of the course, you’ll be able to assess slice-level performance gaps, evaluate optimization outcomes, and make informed decisions about deploying models in real-world edge environments.