Explainable And Interpretable Ai - Techniques And Application

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Free Download Explainable And Interpretable Ai - Techniques And Application
Published: 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.23 GB | Duration: 6h 6m
Learn essential methods for making AI models transparent, understandable, and trustworthy using XAI and IAI techniques.

What you'll learn
Explain the key concepts and differences between Interpretable AI (IAI) and Explainable AI (XAI).
Understand the fundamental principles and distinctions of IAI and XAI, and why they are essential in modern AI applications.
Apply various model-agnostic and model-specific techniques to interpret and explain AI models.
Learn to use tools and libraries such as LIME, SHAP, EBM, and others to provide explanations for complex machine learning models.
Implement practical XAI and IAI solutions using real-world datasets.
Gain hands-on experience in applying XAI and IAI methods to various case studies and projects, enhancing model transparency and trust.
Assess and mitigate biases in AI models to ensure fairness and accountability.
Requirements
Basic understanding of machine learning concepts.
Familiarity with Python programming.
Knowledge of basic statistics and probability.
No prior experience with XAI or IAI is required; all necessary tools and techniques will be taught from scratch.
Description
In the age of artificial intelligence, the ability to understand and trust AI models is important. This comprehensive course on Explainable AI (XAI) and Interpretable AI (IAI) is designed to equip you with the knowledge and skills needed to make your AI models transparent and understandable. Whether you are a data scientist, machine learning engineer, AI researcher, or a business professional, this course will provide you with valuable insights and practical tools to apply in your work.Throughout the course, you will learn the fundamental concepts of XAI and IAI, understand the differences between them, and explore various model-agnostic and model-specific techniques. You will gain hands-on experience with popular tools and libraries such as LIME, SHAP, Explainable Boosting Machine (EBM), and more. Additionally, you will delve into advanced topics like bias mitigation, fairness, adversarial robustness, and feature engineering for interpretability.Key learning objectives include:Understanding the key concepts and differences between XAI and IAI.Applying various model-agnostic and model-specific techniques to interpret and explain AI models.Implementing practical XAI and IAI solutions using real-world datasets.Assessing and mitigating biases in AI models to ensure fairness and accountability.By the end of this course, you will have a solid foundation in interpreting and explaining AI models, enabling you to enhance transparency and trust in AI applications. Join us on this educational journey to unlock the potential of explainable and interpretable AI.Enroll now and take the first step towards mastering XAI and IAI!
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Introduction to Interpretable AI (IAI) and Explainable AI (XAI)
Lecture 2 What is Interpretable AI (IAI)?
Lecture 3 What is Explainable AI (XAI)?
Lecture 4 Importance of Interpretability and Explainability in AI
Lecture 5 Key Differences Between IAI and XAI
Section 3: Fundamentals of AI and Machine Learning
Lecture 6 Supervised Learning
Section 4: Python Programming (Optional)
Lecture 7 What is Python?
Lecture 8 Anaconda & Jupyter & Visual Studio Code
Lecture 9 Google Colab
Lecture 10 Environment Setup
Lecture 11 Python Syntax & Basic Operations
Lecture 12 Data Structures: Lists, Tuples, Sets
Lecture 13 Control Structures & Looping
Lecture 14 Functions & Basic Functional Programming
Lecture 15 Intermediate Functions
Lecture 16 Dictionaries and Advanced Data Structures
Lecture 17 Modules, Packages & Importing Libraries
Lecture 18 Exception Handling & Robust Code
Lecture 19 File Handling
Lecture 20 OOP
Lecture 21 Data Visualization Basics
Lecture 22 Advanced List Operations & Comprehensions
Section 5: Model-Agnostic Interpretation Methods
Lecture 23 LIME (Local Interpretable Model-agnostic Explanations)
Lecture 24 SHAP (SHapley Additive exPlanations)
Section 6: Closing
Lecture 25 The End
Data scientists and machine learning engineers interested in making their models more interpretable and explainable.,AI researchers and practitioners looking to ensure their models are transparent and fair.,Business professionals and decision-makers who want to understand the implications of AI decisions.,Students and academics studying artificial intelligence, machine learning, and data science who want to learn about the latest developments in explainability and interpretability.


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