Explainable Al (XAI) with Python

Explainable Al (XAI) with Python, available at $74.99, has an average rating of 3.96, with 56 lectures, 5 quizzes, based on 317 reviews, and has 3344 subscribers.

You will learn about Importance of XAI in modern world Differentiation of glass box, white box and black box ML models Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques Trade-off between accuracy and interpretability Application of InterpretML package from Microsoft to generate explanations of ML models Need of counterfactual and contrastive explanations Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets. What-if tool from Google to analyze data points and to generate counterfactuals This course is ideal for individuals who are Students taking Machine Learning Course or Artificial Intelligence Course or Students who are looking to make career in AI or Beginner Python programmers who already have some foundational knowledge with machine learning libraries. or 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 or Data analysts and data scientists that want an introduction to explainable AI tools and techniques using Python for machine learning models. It is particularly useful for Students taking Machine Learning Course or Artificial Intelligence Course or Students who are looking to make career in AI or Beginner Python programmers who already have some foundational knowledge with machine learning libraries. or 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 or Data analysts and data scientists that want an introduction to explainable AI tools and techniques using Python for machine learning models.

Enroll now: Explainable Al (XAI) with Python

Summary

Title: Explainable Al (XAI) with Python

Price: $74.99

Average Rating: 3.96

Number of Lectures: 56

Number of Quizzes: 5

Number of Published Lectures: 53

Number of Published Quizzes: 5

Number of Curriculum Items: 62

Number of Published Curriculum Objects: 59

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

• Importance of XAI in modern world
• Differentiation of glass box, white box and black box ML models
• Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques
• Trade-off between accuracy and interpretability
• Application of InterpretML package from Microsoft to generate explanations of ML models
• Need of counterfactual and contrastive explanations
• Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss
• Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.
• What-if tool from Google to analyze data points and to generate counterfactuals

Who Should Attend

• Students taking Machine Learning Course or Artificial Intelligence Course
• Students who are looking to make 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.

Target Audiences

• Students taking Machine Learning Course or Artificial Intelligence Course
• Students who are looking to make 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.