Complete Data Annotation and Machine Learning Course 2026

Explore the four data annotation types: text, image, video, and audio. Focus on image annotation basics with a beginner project using bounding boxes, semantic segmentation, and landmark annotation.

Explore annotation tools and platforms across text, image, video, and audio; compare open source and paid options and learn best practices for efficient labeling workflows.

Set up the CVAT annotation tool for image and video tasks, exploring projects like image classification, object detection, semantic and instance segmentation, point clouds, LiDAR, 3D cuboid, and skeleton annotation.

Collect raw images and annotate them with bounding boxes in CVAT to enable object detection and classification of animals such as fox and elephant.

Learn to measure data quality metrics in data annotation to ensure reliable training data. Use accuracy, inter-annotator agreement, precision, recall, and F1 score with clear guidelines and dashboards.

Perform a hands-on quality check of annotated datasets, validating labels and bounding boxes before machine learning training. Fix label errors and adjust boxes to ensure complete object coverage.

Explore the fundamentals of machine learning, from training data sets and pattern discovery to automatic output predictions, and compare supervised, unsupervised, and reinforcement learning with real-world applications.

Explore the machine learning workflow from raw data collection through data anonymization, annotation, train/test splitting, model training, and deployment to real-world applications.

Train an image classification model in your browser using Teachable Machine, without coding, by gathering dog and cat images, training, and exporting the model.

Learn to train annotated image datasets using the CWAT tool and Ultralytics YOLO v8. Create folder structures, export annotations, and run training in Google Colab to detect objects.

Develop a recycling and waste management model that detects glass, plastic, and tin containers to sort them. Apply it to inventory management and public safety at events.

Train annotated image datasets by exporting labels to YOLO format and organizing images, labels, and a Colab config; train with ultralytics YOLO v8 in Colab.

Evaluate real object detection results from a trained YOLO model, interpreting F1, precision, recall, and map curves to identify strengths, weaknesses, and actions to improve performance.

Deploy a trained YOLO model from training to inference, integrating it into local, web, and mobile apps and live camera feeds for object detection with bounding boxes and confidence scores.

Build a strong foundation in data and annotation types, and practice applying these insights to real datasets to improve model performance in real-world AI systems.