Hands-On Explainable AI (XAI) with Python

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Explaining Machine Learning with Facets

Lack of the right data often poisons an artificial intelligence (AI) project from the start. We are used to downloading ready-to-use datasets from Kaggle, scikit-learn, and other reliable sources.

We focus on learning how to use and implement machine learning (ML) algorithms. However, reality hits AI project managers hard on day one of a project.

Companies rarely have clean or even sufficient data for a project. Corporations have massive amounts of data, but they often come from different departments.

Each department of a company may have its own data management system and policy. When finally you obtain a training dataset sample, you may find that your AI model does not work as planned. You might have ...