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