Synthetic Data: Advanced Concepts and Applications

38mAdvanced2023-01-03

Authors

Michael Galarnyk

Python Instructor and Blogger

Madecraft

Full-Service Learning Content Company

Course details

We use data to make decisions, understand trends, and optimize processes. And data is a key component of machine learning. But collecting data that has the quality, quantity, and diversity that you need for your machine learning use case can be time-consuming and difficult. In this course, discover how you can use synthetic data—artificially generated information, not data collected from real world events—for machine learning. Learn how to generate synthetic data, how to combine it with real data, and important differences between synthetic and real data. Get tips and tricks to optimize your training performance, and find out how to recognize synthetic data problems. Check out this course to learn how to recognize when synthetic data is needed, how to select data generation methods, and leverage various model-training strategies.

Skills covered

Machine LearningGenerative AIArtificial Intelligence (AI)One-Off

Concepts

0. Introduction

01 - Unlock the power of synthetic data

1. Introduction to Synthetic Data

02 - Articulating synthetic data's value
03 - Required background knowledge

2. Generating Synthetic Data

04 - Defining synthetic data
05 - Generating synthetic data
06 - Defining domain gaps
07 - Reducing the domain gap
08 - What is generative AI
09 - Real data errors and solutions
10 - Synthetic data for edge cases

3. Implementing Synthetic Data

11 - Real-world label scarcity
12 - Leveraging pre-training and fine-tuning
13 - Leveraging joint training
14 - Applying data sampling techniques
15 - Privacy with synthetic data
16 - Machine learning with synthetic data

Conclusion

17 - Going further with synthetic data