Lessons
Session 01: Web Fundamentals — From URL to Browser
Understand how the web works from the ground up: Client-Server, HTTP, the three frontend layers, APIs, and HTTP status codes — all through a PM lens.
Session 03: APIs in Depth — REST, Auth, and Rate Limiting
Read API docs, understand how auth works, and use Postman to test APIs — without writing a single line of code.
Session 04: System Architecture — Monolith, Microservices, and Queues
Understand how architecture decisions shape delivery speed, and ask the right questions when an architect proposes changing the system.
Session 05: Cloud & Deployment — Docker, CI/CD, and Release Planning
Understand the pipeline from code to production, why staging exists, and how to plan releases without getting blocked by DevOps.
Session 06: Security for PMs — Auth, OWASP, and Data Privacy
Understand the common security vulnerabilities PMs accidentally introduce when writing specs, and how to write security requirements the right way.
Session 07: Performance & Scaling — Bottlenecks, CDN, and Web Vitals
Read a Lighthouse report, pin down the real bottleneck, and write performance requirements you can actually measure.
Session 08: Working with Eng — Estimation, Technical Debt, and Incident Response
Understand why estimates are always wrong, how to frame technical debt as product risk, and the PM role in incident response.
Session 09: AI for PMs — Understand AI to Make Better Product Decisions
Understand how LLMs work, when to use AI, build vs buy, and the risks PMs need to know when integrating AI into a product.
Lesson 01: Why Annotation Matters
Understand why data annotation is the highest-leverage activity in applied ML, how the tool landscape has evolved, and where AnyLabeling fits.
Lesson 02: Installation & First Labels
Install AnyLabeling via pip, binary, or GPU-accelerated package, tour the interface, and annotate your first image in under five minutes.
Lesson 03: Manual Annotation Deep Dive
Master every annotation type in AnyLabeling — rectangles, polygons, circles, lines, points, and rotated bounding boxes — with the techniques that make manual labeling fast and precise.
Lesson 04: SAM Auto-Labeling
Use Segment Anything models (SAM, SAM 2, SAM 2.1, SAM 3, MobileSAM) for one-click segmentation — point prompts, rectangle prompts, text prompts, and the workflow that makes it fast.
Lesson 05: YOLO Auto-Labeling
Use YOLOv5 and YOLOv8 models to auto-generate bounding boxes and segmentation masks, then review and correct the results for production-quality datasets.
Lesson 06: Text, OCR & Key Information Extraction
Annotate text in images — detection regions, transcription, and structured Key Information Extraction (KIE) for documents, receipts, and scene text.
Lesson 07: Export Formats & Pipelines
Export annotations to COCO, YOLO, Pascal VOC, and CreateML formats — understand when to use each one and build conversion scripts for your training pipeline.
Lesson 08: Custom Models for Auto-Labeling
Load your own ONNX models into AnyLabeling — convert from PyTorch or Ultralytics, write the config.yaml, and use domain-specific models as auto-labeling backends.
Lesson 09: Writing Annotation Guidelines
Write annotation guidelines that eliminate ambiguity, handle edge cases, and produce consistent labels across annotators — the most underrated skill in applied ML.
Lesson 10: Active Learning Pipelines
Build a label-train-relabel loop that improves both your model and your dataset with every iteration — confidence-based sampling, model-in-the-loop annotation, and knowing when to stop.
Lesson 11: Scaling Annotation for Teams
Scale annotation beyond a single person — multi-annotator workflows, quality assurance reviews, conflict resolution, and dataset versioning for production ML teams.
Lesson 01: What is DaisyKit?
Understand the motivation behind DaisyKit, its graph-based pipeline architecture, and how it compares to frameworks like MediaPipe and OpenCV.
Lesson 02: Installation & First Program
Install DaisyKit on Linux or Windows, understand the get_asset_file model registry, and run your first face detection flow on a static image.
Lesson 03: Face Detection & Landmarks
Run real-time face detection with YOLO Fastest and 68-point facial landmark regression with PFLD — including mask detection — using FaceDetectorFlow.
Lesson 04: Human Pose Estimation
Detect 17 body keypoints in real time using SSD-MobileNetV2 person detection and Google MoveNet Lightning — with HumanPoseMoveNetFlow.
Lesson 05: Background Matting
Replace video backgrounds in real time using AI portrait segmentation with BackgroundMattingFlow — no green screen required.
Lesson 06: Hand Pose Detection
Detect 21 3D hand keypoints per hand in real time using YOLOX hand detection and Google MediaPipe hand pose model — with HandPoseDetectorFlow.
Lesson 07: Object Detection with YOLOX
Detect 80 COCO object classes in real time using YOLOX via ObjectDetectorFlow — and learn how to filter classes and use custom-trained models.
Lesson 08: Barcode & QR Code Scanning
Scan QR codes, barcodes, and 2D codes in real time using BarcodeScannerFlow — powered by ZXing-CPP with no deep learning required.
Lesson 09: Mobile Deployment
Run all DaisyKit AI flows on Android and iOS devices using the prebuilt native SDKs — no server, no cloud, fully on-device inference.
Lesson 10: Custom Models & C++ SDK
Integrate custom NCNN models into DaisyKit flows, use the C++ graph API for concurrent pipelines, and build production-grade AI applications.
Lesson 07: Edge Detection
Learn how to detect edges in images using Sobel, Laplacian, and Canny edge detectors in OpenCV.
Lesson 08: Thresholding & Morphological Operations
Learn how to segment images with thresholding and refine shapes with morphological operations in OpenCV 4.x.
Lesson 09: Contours & Shape Analysis
Find, draw, and analyze contours to detect and measure shapes in images using OpenCV 4.x.
Lesson 10: Feature Detection & Matching
Detect keypoints and match features across images using SIFT, ORB, and FLANN in OpenCV 4.x.
Lesson 11: Video Processing & Optical Flow
Read, write, and process video streams. Track motion with background subtraction and optical flow in OpenCV 4.x.
Lesson 12: Deep Learning with the OpenCV DNN Module
Run YOLO26, ONNX, and other deep learning models for object detection and image classification — using the Ultralytics API and the OpenCV DNN module.
Lesson 13: Real-World Project — Real-Time Object Tracker
Build a complete real-time multi-object tracking application using YOLO26 detection and OpenCV trackers for persistent IDs.
Lesson 06: Image Filtering for Image Enhancement
Apply average, Gaussian, median, and bilateral blurring, then sharpen images with high-pass filters, unsharp masking, and the Laplacian operator.
Lesson 05: Color Spaces
Master RGB, HSV, Grayscale, and LAB color spaces — convert between them and use HSV masking for real-world color-based object detection.
Lesson 04: Basic Image Processing
Load, display, resize, rotate, crop, and flip images with OpenCV — using real downloadable test images.
Lesson 03: What is OpenCV?
Install OpenCV, run your first program, and understand the BGR color convention that underpins every OpenCV operation.
Lesson 02: What is Computer Vision?
Understand how machines perceive images, explore real-world CV applications, and see a live YOLO26 object detection example.
Lesson 01: Introduction to OpenCV Course
Course overview and outline — what you will build, what tools you need, and how the lessons are structured.