Synthetic Data: Advanced Concepts and Applications
Synthetic Data: Advanced Concepts and Applications
What you’ll learn
Skills you’ll gain
As technology continues to evolve, synthetic data has begun to replace real-world data, and its vast benefits are still in the early stages of being uncovered. In this course, python and data science expert Michael Galarnyk teaches advanced synthetic data concepts and applications. Michael begins the course by defining synthetic data and explaining some of the many benefits of leveraging synthetic data in a professional setting. Then, Michael goes on to explain how to balance synthetic data in datasets and how to leverage generative AI for synthetic data generation. Michael concludes the course by walking you through strategies to effectively implement synthetic data. After completing this course, you'll be able to define synthetic data, describe its relationship to real-world data, identify its potential benefits, and recognize its potential applications across industries.
Syllabus
Download syllabus-
1
Articulating synthetic data's value In many domains, collecting, and especially labeling high quality, real-world data can be time consuming, difficult, expensive, dangerous, or even impossible. 2m
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2
Required background knowledge Generating and training models with synthetic data requires some basic knowledge of statistics and machine learning. 1m
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Defining synthetic data Synthetic data is data that is artificially generated rather than collected from the real-world. 2m
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2
Generating synthetic data Synthetic data has many use cases and it is not all generated in the same way. 2m
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3
Defining domain gaps A domain gap is the difference between two distinct but related datasets. 2m
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4
Reducing the domain gap Reducing the domain gap between real and synthetic data can lead to improved machine learning performance. 2m
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5
What is generative AI Generative AI represents a subset of AI algorithms that leverages machine learning, especially deep learning to produce new content. 2m
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6
Real data errors and solutions Real datasets can have label errors. 2m
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Synthetic data for edge cases A lot of machine learning use cases require datasets that are comprehensive, sufficiently large, high quality, diverse, and accurately representative of a problem space it’s intended to model. 2m
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Real-World Label Scarcity Synthetic data. 3m
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2
Leveraging pre-training and fine-tuning How do you incorporate synthetic data into your model training strategy? 2m
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3
Leveraging joint training Once you have your real and synthetic data, how do you actually use them together to train a model? 2m
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4
Applying data sampling techniques Data sampling can be defined as the process of selecting a subset of data for analysis. 2m
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5
Privacy with synthetic data While synthetic data doesn't have the same privacy concerns, it is still something that needs to be considered. 2m
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6
Machine learning with synthetic data The Machine Learning Development Cycle is a roadmap that guides you in creating and improving machine learning models. 2m
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Going further with synthetic data Thank you for watching this course! 1m
Certificate
Certificate of Completion
Awarded upon successful completion of the course.
Instructor
Michael Galarnyk
Michael is a recognized Python instructor and blogger.He taught University of California, San Diego, Extension, and Stanford Continuing Studies. Michael is constantly expanding his knowledge of the latest Python tools and technologies.You can find Michael on Medium or LinkedIn.
Michael Galarnyk
Python Instructor and Blogger
Accreditations
Link to awards
How GoSkills helped Chris
I got the promotion largely because of the skills I could develop, thanks to the GoSkills courses I took. I set aside at least 30 minutes daily to invest in myself and my professional growth. Seeing how much this has helped me become a more efficient employee is a big motivation.
