Online course on Explainable Artificial Intelligence (XAI) with Python

[Prashant Singh Rana]

Dear All,

Explore the new domain in Machine Learning.

---------- Forwarded message ---------
From: PARTEEK BHATIA <parteek...@thapar.edu>

Dear All,

Hope that this email finds you well.

I am pleased to inform you that here at TAU, I got an opportunity to purpose and run a course on Explainable Artificial Intelligence (XAI) with Python. 

Based on learning of this course, I have created an online course on Explainable Artificial Intelligence (XAI) with Python on the Udemy platform. I am sharing this course with you.

Objectives of the course

This course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on AI models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This course discusses tools and techniques using Python to visualize, explain, and build trustworthy AI systems. 

Contents

This course covers the working principle and mathematical modeling of LIME (Local Interpretable Model Agnostic Explanations), SHAP (SHapley Additive exPlanations) for generating local and global explanations. It discusses the need for counterfactual and contrastive explanations, the working principle, and mathematical modeling of various techniques like Diverse Counterfactual Explanations (DiCE) for generating actionable counterfactuals. The concept of AI fairness and generating visual explanations are covered through Google's What-If Tool (WIT).  This course covers the LRP (Layer-wise Relevance Propagation) technique for generating explanations for neural networks.

All the techniques are explained through hands-on sessions so that learns can clearly understand the code and can apply it comfortably to their AI models. The dataset and code used in implementing various XAI techniques are provided to the learners for their practice.

Who this course is for:

• Researchers using or planning to use machine learning in their research area

• Students and teachers taking Machine Learning Course or Artificial Intelligence Course

• Students who are looking to make a career in AI

• Beginner Python programmers who already have some foundational knowledge with machine learning libraries.

• Researchers who already use Python for building AI models and can benefit from learning the latest explainable AI techniques to generate explanations of their models

• Data analysts and data scientists that want an introduction to explainable AI tools and techniques using Python for machine learning models.

Link of the course for you

I am sharing the coupon code and link for this course. Please use this link to register and explore the course.

Explainable Artificial Intelligence (XAI) with Python | Udemy

This will make this course completely free, I am also sharing a few Introductory videos on XAI.

Link for Introductory videos

https://youtu.be/qkC2VTSgVw0

https://youtu.be/bT7XVCpXFqY

Please review this course and recommend it to your students if you find it useful.

You all are always in our thoughts and we miss all of you. We always feel gratitude to our Thapar fraternity.

God bless you, wish you a healthy and happy year ahead.

Thanks and regards

Dr. Parteek Kumar Bhatia

Visiting Professor, LAMBDA Lab, TAU, Israel

Professor 

Department of Computer Science & Engineering

Thapar Institute of Engineering & Technology, Patiala

www.parteekbhatia.com

Member of IEEE, ACM and ISTE

Mobile No.9876175046

[Usha Rani Nelakuditi]

Thank you so much sir

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With Best Regards

Prof. Usha Rani.Nelakuditi

Professor  - Vignan's Foundation for Science, Technology and Research

Secretary, Guntur Subsection (2020-21)

Member -CIS/GRSS Society, Hyderabad Section (2020-21)

Member - Humanitarian Technology Activites (HTA), IEEE Region 10, 202