Coursera
MOOC / Non-credit
0
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.
C
55/100
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- What the provider tells you
- 31/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
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
Machine Learning
Artificial Intelligence
#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
$49.00
Price shown by Coursera — confirm on their site.
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