Applied Machine Learning Explainability Techniques
Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
Created by Aditya Bhattacharya
Gain practical skills to make machine learning models more transparent and trustworthy using real-world explainability techniques. Learn how to apply methods like LIME and SHAP to clarify model decisions and bridge the gap between AI systems and end users.
Packt | Jul 2022 | 306 min
What You Will Learn
You will start by exploring the core ideas behind explainable AI and why it matters. Through hands-on Python examples, you will practice using popular frameworks to interpret model outputs. Along the way, you will learn to evaluate different explanation methods and apply guidelines for real-world problem solving.
Key Features
- Apply LIME and SHAP to interpret and explain machine learning model predictions
- Design user-focused explainable ML systems for industrial and research applications
- Implement best practices to build transparent and trustworthy AI solutions
Target Audience
This content is ideal for data scientists, ML engineers, product managers, and researchers with a working knowledge of Python and machine learning. If you want to make your AI models more understandable and accessible to both technical and non-technical audiences, you will find actionable strategies and tools here.
