Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions

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1. Introduction

001. Exploring the world of explainable AI and inte.mp4

01:02

002. Target audience.mp4

01:18

003. What you should know.mp4

01:03

2. What Are XAI and IML

004. Understanding the what and why your mo.mp4

04:28

005. Variable importance and reason codes.mp4

02:22

006. Comparing IML and XAI.mp4

04:23

007. Trends in AI making the XAI problem mo.mp4

06:18

008. Local and global explanations.mp4

02:23

009. XAI for debugging models.mp4

02:26

010. KNIME support of global and local expl.mp4

02:22

3. Why Isolating a Variables Contribution Is Difficult

011. Challe.mp4

08:48

012. Challe.mp4

03:30

013. Rashom.mp4

04:42

4. Black Box Model 101

014. What qualifies as a black box.mp4

02:51

015. Why do we have black box models.mp4

04:20

016. What is the accuracy interpretability t.mp4

04:31

017. The argument against XAI.mp4

03:02

5. Introduction to KNIME for XAI and IML

018. Introducing KNIME.mp4

04:08

019. Building models in KN.mp4

05:13

020. Understanding looping.mp4

03:01

021. Where to find availab.mp4

02:53

6. XAI Techniques Global Explanations

022. Providing global explana.mp4

04:58

023. Using surrogate models f.mp4

01:52

024. Developing and interpret.mp4

04:52

025. Permutation feature impo.mp4

01:11

026. Global feature importanc.mp4

06:54

7. Techniques for Local Explanations

027. Developing an intuition f.mp4

04:35

028. Introducing SHAP.mp4

01:48

029. Using LIME to provide loc.mp4

02:23

030. What are counterfactuals.mp4

02:27

031. KNIME's Local Explanation.mp4

04:03

032. XAI View node demonstrati.mp4

06:41

8. IML Techniques

033. General advice for better IML.mp4

04:39

034. Why feature engineering is critical for IML.mp4

02:10

035. CORELS and recent trends.mp4

04:59

9. Conclusion

036. Continuing to explore XAI.mp4

01:21

Description

Data scientists and machine learning professionals have to stay apace with the latest techniques and approaches in the field. In this course, instructor Keith McCormick shows you how to produce explainable AI (XAI) and interpretable machine learning (IML) solutions.

Learn why the need for XAI has been rapidly increasing in recent years. Explore available methods and common techniques for XAI and IML, as well as when and how to use each. Keith walks you through the challenges and opportunities of black box models, showing you how to bring transparency to your models and using real-world examples that illustrate tricks of the trade on the easy-to-learn, open-source KNIME Analytics Platform. By the end of this course, you’ll have a better understanding of XAI and IML techniques for both global and local explanations.

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