Local Interpretable Model-Agnostic Explanations Courses & Certifications

Overview

Curriculum

Mentor

Reviews

Discover how to make complex outputs understandable with LIME (Local Interpretable Model-agnostic Explanations). This course introduces a powerful method that explains individual outcomes by building simple, interpretable models around specific inputs. Learn how LIME creates perturbed samples, assigns importance scores, and reveals which inputs drive each result. With clear numerical examples and practical exercises, you’ll gain a strong grasp of each step in the explanation process. Implementation is covered using both Python and MATLAB, supported by real-world scenarios from domains like healthcare, finance, and monitoring systems. Perfect for anyone working with complex decision systems and looking to bring clarity to individual predictions.

1.1

XAI - Definition and Importance
Working of XAI
Applications of XAI

2.1

Perturbation-Based Feature Attribution Methods
Propagation-Based Feature Attribution Methods
Concept-Based Explainability Methods

3.1

Interpretable Machine Learning Models
Deep Learning Models in XAI
Future of XAI in Machine Learning

4.1

Introduction to LIME
Steps Involved in LIME
LIME for Different Types of Data

5.1

LIME for Tabular Data
LIME for Text Data
LIME for Image Data

6.1

Matlab Code Development

7.1

Python Code Development

8.1

Matlab
Python

4.7

5 Ratings

Reviews

Ananya Gupta

( Great explanation of model interpretability. )

"This course explains how LIME helps interpret machine learning predictions. The examples clearly demonstrate how local explanations can reveal which features influence model outputs."

Rohit Bansal

( Very insightful XAI course. )

"Understanding black-box models is difficult, but this course explains LIME in a very practical way. The case studies made the concept easy to understand."

Meera Joshi

( Clear and practical course. )

"The instructor demonstrates how LIME generates explanations for predictions. It helped me better understand the importance of explainability in AI systems."

Vikram Singh

( Excellent interpretability course. )

"A well-structured course that explains local interpretability techniques through clear examples and real-world applications."

Divya Nair

( Highly recommended for data scientists. )

"The course provides deep insight into how LIME can be used to analyze model predictions and build trust in machine learning systems."

₹1000

₹6000

Live Learning Sessions - 2hrs

Join live classes to gain deeper insights and direct feedback from expert instructors.

This course includes:

Validity:

60 days
Validity:

60 days
Category:

Explainable AI (XAI)
Lessons:

8
Students:

1600
Language:

English
Certificates:

Yes