Data Labeling for Machine Learning

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Janani Ravi

1:54:46

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01 - Introduction

01 - The need for data labeling.mp4

04:55

02 - 1. Get Started with Data Labeling

01 - The data labeling process.mp4

03:12

02 - Approaches to data labeling.mp4

04:02

03 - Data labeling challenges, best practices, and use cases.mp4

02:37

04 - Data labeling with Azure ML.mp4

01:55

05 - Setting up an Azure ML workspace.mp4

03:11

06 - Setting up an image labeling project Creating data assets.mp4

04:51

07 - Setting up an image labeling project Configuring settings.mp4

04:53

08 - Manual image labeling and review.mp4

04:49

09 - Manual labeling progress checks.mp4

03:23

03 - 2. Perform Manual and ML-Assisted Data Labeling on Azure

01 - Automated machine learning for image classification.mp4

05:07

02 - Examining model training metrics.mp4

03:06

03 - Data labeling project insights.mp4

02:20

04 - ML assisted labeling with clustering and pre-labeling.mp4

03:06

05 - Configuring inference for new training runs.mp4

02:41

06 - Exploring the labeled dataset.mp4

02:15

04 - 3. Use Snorkel for Data Labeling

01 - Programmatic labeling with Snorkel.mp4

05:24

02 - Installing Python libraries.mp4

02:56

03 - Exploring the spam ham dataset.mp4

06:26

04 - Writing and analyzing labeling functions.mp4

06:57

05 - Exploring other labeling functions.mp4

05:25

06 - Programmatic labeling using the majority label voter.mp4

03:22

07 - Scoring and comparing the label models.mp4

03:16

05 - 4. Create Diverse Labeling Functions and Models in Snorkel

01 - Increasing the number of labeling functions.mp4

03:57

02 - Using sentiment and parts of speech tagging in labeling functions.mp4

07:53

03 - Evaluating labeling function metrics on test data.mp4

03:07

04 - Using all labeling functions to label data.mp4

06:00

05 - Training a classifier on programmatically generated labels.mp4

02:19

06 - Conclusion

01 - Summary and next steps.mp4

01:21

Description

Almost 2.5 quintillion bytes of data are produced every day—mostly raw, unlabeled data—but supervised learning techniques for machine learning require data to be labeled in order to use it for training. This makes data labeling, time-consuming and expensive though it may be, a vital part of machine learning. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through how to get started with data labeling. Learn about different approaches to data labeling, as well as the challenges, best practices, and use cases that go with it. Go over data labeling with Azure ML, and find out how to set up an image labeling project and perform manual image labeling, reviews, and progress checks. Step through the full process of performing manual and ML-assisted data labeling on Azure, then explore how to use Snorkel for data labeling, including how to create diverse labeling functions and models.

This course was created by Janani Ravi. We are pleased to host this training in our library.

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Machine Learning

Janani Ravi

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.