Data Sciencebeginner

Hands On Data Annotation Applied Machine Learning

Name: Hands On Data Annotation Applied Machine Learning
Availability: InStock

0 students 3h 52m 59 lectures

DCreated by Dafluma Instructor

Last updated Recently updated English Certificate of completion

What you will learn

The instructor has not published learning outcomes yet.

Requirements

No strict prerequisites listed.

Course Curriculum

5 sections. 59 lectures. 3h 52m total.

Section 1: Introduction

1 lectures

Section 2: Data Annotation

7 lectures

What is data annotation1 min
Why is data annotation important in machine learning1 min
Principles of data annotation3 min
Types of data annotation1 min
Data storage structure3 min
Data annotation tools and formats3 min
Data annotation quality3 min

Section 3: Data Annotation for Computer Vision

15 lectures

Labeling for image classification in notebook using Pigeon6 min
Data annotation with Computer Vision Annotation Tool CVAT4 min
Annotation for image classification using CVAT6 min
Data labeling for object detection using CVAT5 min
Model assisted labeling for object detection using CVAT6 min
Polygon masking in CVAT for semantic segmentation4 min
Segment Anything Model SAM for Polygon masking in CVAT4 min
Data annotation with Roboflow Annotate setup2 min
Single label for image classification in Roboflow Annotate6 min
Multi label for image classification in Roboflow Annotate5 min
Image labeling for object detection using Roboflow Annotate5 min
Export annotation in a different format in Roboflow Annotate2 min
Smart Polygon in Roboflow Annotate3 min
Model assisted video annotation in Roboflow Annotate3 min
Collaboration in Roboflow Annotate1 min

Section 4: Data Annotation for Natural Language Processing

13 lectures

Text labeling for sentiment analysis in spreadsheets6 min
Data annotation with Universal Data Tool UDT1 min
Named Entity Recognition labeling using Universal Data Tool4 min
Label text for classification using the Universal Data Tool3 min
Text data annotation with Prodigy setup4 min
Manual annotation for named entity recognition with Prodigy5 min
Semi automatic text annotation for NER with Prodigy4 min
Command line text annotation for NER with Prodigy4 min
labeling for text classification with Prodigy4 min
Part of speech POS labeling with Prodigy4 min
Sentence boundary labeling with Prodigy3 min
Audio data labeling with Prodigy5 min
Audio data transcription with Prodigy4 min

Section 5: Data Annotation on Cloud Computing Platforms

23 lectures

Data annotation on AWS SageMaker Ground Truth setup4 min
Single label image classification annotation in AWS6 min
Multi label image classification annotation in AWS4 min
Image bounding box annotation for object detection in AWS5 min
Image semantic segmentation annotation in AWS5 min
Video object tracking annotation in AWS6 min
Text labeling for classification in AWS3 min
NER text labeling in AWS4 min
Data annotation on Azure Machine Learning Studio setup2 min
Multi class image classification annotation in Azure ML6 min
Multi label image classification annotation in Azure ML5 min
Image bounding box annotation for object detection in Azure6 min
Image instance segmentation annotation in Azure5 min
Text labeling for classification in Azure5 min
NER text labeling in Azure4 min
Audio transcription in Azure ML Studio3 min
Data annotation on GCP Vertex AI setup3 min
Multi label image annotation in GCP6 min
Image bounding box annotation for object detection in GCP5 min
Text entity annotation in GCP3 min
Text sentiment scale labeling in GCP4 min
Single label text labeling in GCP4 min
Video classification annotation in GCP5 min

Your Instructor

Dafluma Instructor

Published course Dafluma instructor

Instructor profile details are managed by the backend. This course is taught by Dafluma Instructor and kept inside the platform subscription experience.

Course safety and support

Report misleading, unsafe, or rights-sensitive content. For access or payment issues, open a support request instead.

Get support

Report targetReasonDetails

You will be asked to sign in before submitting a report so the safety team can follow up responsibly.