Engineer & Explain AI Model Decisions

Coursera MOOC / Non-credit USD 49

Enroll now →

Engineer & Explain AI Model Decisions

About this course

This course focuses on how to build trustworthy AI systems and explain their decisions effectively. As machine learning and AI grow in complexity, it's essential to not only achieve high accuracy but also to understand and communicate the reasoning behind model predictions. You'll learn to clean and transform data, employ advanced interpretability techniques, and develop a toolkit that helps stakeholders grasp the insights and implications of AI decisions.

What you'll learn

mastery of data transformation techniques
ability to clean and structure conversational logs
understanding of explainability techniques like SHAP
skills in identifying and remediating biases in AI models
creation of a stakeholder-ready interpretability report

Course objectives

to empower professionals in building ethical AI systems
to enhance transparency in AI model deployments
to provide actionable insights for stakeholders

Skills you'll gain

python scikit-learn machine learning ai ethics stakeholder communication predictive modeling data cleaning data transformation feature engineering shap model diagnostics model interpretability tensors tf-idf bias remediation

Related courses

Machine Learning for Trading

Data Science Foundations

Generative AI for Executives and Business Leaders

Intelligent Agents and Search Algorithms

CPMAI Exam: 570+ Updated Practice Questions for 2026

Generative AI Cybersecurity & Privacy for Leaders

Internet of Things and AI Cloud

The Complete Quantum Computing Course

Course details are provided by the platform and may change — always confirm on the provider's site. Links may be affiliate links.