Subject – Human-Centered AI and Explainability
Industry – Machine Learning and AI
Introduction:
Welcome to the eLearning course on “Explainable AI Models and Techniques” offered by T24Global Company. In this course, we will delve into the fascinating world of Machine Learning and Artificial Intelligence (AI) to understand the importance of explainable AI models and techniques.
Machine Learning and AI have revolutionized various industries, from healthcare to finance, by enabling computers to learn from data and make accurate predictions or decisions. However, as these technologies become more sophisticated, it becomes increasingly challenging to understand the inner workings of AI models and interpret their decisions. This lack of transparency can hinder trust, limit adoption, and even lead to unintended consequences.
To address this challenge, the field of explainable AI has emerged. Explainable AI aims to develop models and techniques that not only make accurate predictions but also provide understandable explanations for their decisions. By understanding why an AI model made a particular decision, we can gain insights into its underlying reasoning and ensure its fairness, accountability, and transparency.
In this course, we will explore various aspects of explainable AI models and techniques. We will start by understanding the basics of Machine Learning and AI, including popular algorithms and their applications. Next, we will delve into the concept of explainability and why it is crucial in the context of AI. We will discuss the trade-offs between accuracy and interpretability and explore different approaches to achieving explainability in AI models.
One of the key topics we will cover is model-agnostic techniques, which can be applied to any AI model to provide explanations. We will explore methods such as feature importance, partial dependence plots, and SHAP values, which help us understand the factors that contribute to a model’s predictions. Additionally, we will discuss rule-based models, such as decision trees and rule lists, which inherently provide interpretable explanations.
Furthermore, we will examine the challenges and limitations of explainable AI, including the balance between simplicity and accuracy, the potential for adversarial attacks, and the ethical considerations surrounding transparency. We will also discuss the legal and regulatory landscape related to explainability in AI, such as the General Data Protection Regulation (GDPR) and other emerging guidelines.
Throughout the course, we will provide hands-on exercises and practical examples to reinforce your learning. By the end of this course, you will have a solid understanding of the importance of explainable AI models and techniques, and you will be equipped with the knowledge to apply them in real-world scenarios.
We are excited to have you on this learning journey with us. Let’s dive into the world of explainable AI and unlock the potential of transparent and trustworthy AI models.
NOTE – Post purchase, you can access your course at this URL – https://mnethhil.elementor.cloud/courses/explainable-ai-models-and-techniques/ (copy URL)
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Lessons Included