Visualization Best Practices for Explainable AI

Overview

Limited-Time Offer: Up to 75% Off Coursera Plus!

7000+ certificate courses from Google, Microsoft, IBM, and many more.

This course focuses on teaching best practices for visualizing Explainable AI concepts using Jupyter Notebooks, Python, and Power BI. The learning outcomes include understanding machine learning terms, explaining charts to business stakeholders, and walking through real-world examples from various industries. The course covers skills such as interpreting machine learning vs. AI, utilizing Automated Machine Learning (AutoML), explaining model results, visualizing model workings, investigating model training data, and evaluating model performance. The teaching method involves a walk-through of real-world examples and practical demonstrations. This course is intended for individuals interested in effectively communicating AI concepts and model results to business stakeholders using visualizations.

Syllabus

Intro
Technical Assistance
Machine Learning vs. Artificial Intelligence (AI)
Why do they call it "Machine Learning"? Machines learn from data to make predictions on new data
Two stage machine learning process How does it work?
Automated Machine Learning (AutoML) 3 simple steps
Explain results Need to speak data using a common language
Explainable Al, is it possible?
Helps avoid incompleteness issues Explanations fundamentally help identify gaps in problem formalization - incompleteness
Visualize how models work
How to understand a model
What data did the model use? Understand model data source limitations
Investigate model training data
Understand what matters
Explore relationships between variables
Examine decision rules Rules Fit Classifiers
Discover business rules from text fields
Analyze prediction explanations Global
Evaluate model performance
Understand how well a model fits the data
Examine where models make mistakes
Detect model changes over time
Time series
Visualize probability Workflows
Resources