Upcoming Course: Synthetic Data, Explainable and Generative AI

The performance of machine learning algorithms such as classification, clustering, regression, decision trees or neural networks can be significantly improved with synthetic data. It enriches training sets, allowing you to make predictions or assign a label to new observations that are significantly different from those in your dataset.

It is very useful if your training set is small or unbalanced. It also allows you to test the limits of your algorithms and find examples where it fails to work (for instance, failing to identify spam). Or deal with missing data or create confidence regions for parameters. I will show how to design rich, good quality synthetic data to meet all these goals. In particular, I illustrate how to rebalance data sets with synthetic data when some categories have very few observations (in fraud detection or clinical trials), how to remove biases by including good-quality synthesized minority people in your data, and how to anonymize your data to boost security and for compliance with privacy laws.

In this course taught by Dr. Vincent Granville, you will learn how to create your own synthetic data in Python. One example includes a real-life insurance data set: using copulas, you will be able to create an alternate (synthetic) data set that matches extremely well the distribution of the observations in your training set – including all the correlations – and why someone would want to do that. Other examples including computer vision, time series and animated data sets. For instance, agent-based modeling and evolutionary processes (such as virus spreading) where you will also learn how to create insightful data videos in Python.

Dr Granville is consistently ranked by various media outlets as one of the top machine learning scientists in the world. To learn more about the course, follow this link.