| File Name: | Explainable and Interpretable Artificial Intelligence : 1 |
| Content Source: | https://www.udemy.com/course/explainable-and-interpretable-artificial-intelligence-1 |
| Genre / Category: | Other Tutorials |
| File Size : | 1.5 GB |
| Publisher: | Advancedor Academy |
| Updated and Published: | August 31, 2025 |
Artificial Intelligence is powerful, but many machine learning models act like “black boxes.” We see the predictions, but not always the reasoning behind them. This lack of transparency makes it hard to trust and explain AI decisions in real-world applications such as healthcare, finance, or business.
This course introduces you to the foundations of Explainable and Interpretable AI (XAI), focusing on practical, model-agnostic interpretation methods. You’ll start with the basics of explainability and why it matters. From there, you’ll explore two of the most widely used techniques: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). These tools help reveal how features contribute to predictions, making complex models easier to understand.
You will then apply these methods in Python through hands-on examples. Working with datasets such as housing prices, text classification, and medical predictions, you’ll see how interpretation methods provide insights into models like Random Forests and neural networks. Along the way, you’ll also learn about additional libraries, including PDP, ELI5, Skater, and Captum, which broaden your toolkit for interpreting models across different contexts.
The course concludes with a recap section, where you revisit SHAP, LIME, and other methods to reinforce your understanding and compare their strengths. By the end of this course, you’ll be equipped with the knowledge and skills to interpret machine learning models, explain their outputs to stakeholders, and build AI systems that are not only accurate but also transparent and trustworthy.
Who this course is for:
- Beginners in data science who want to understand how machine learning models make decisions.
- Developers and engineers interested in building more transparent and trustworthy AI systems.
- Students and professionals in AI/ML looking for practical tools to explain model predictions.
DOWNLOAD LINK: Explainable and Interpretable Artificial Intelligence : 1
Explainable_and_Interpretable_Artificial_Intelligence_1.part1.rar – 1000.0 MB
Explainable_and_Interpretable_Artificial_Intelligence_1.part2.rar – 597.3 MB
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