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

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size360.5 MB
  • Uploaded Byfreecoursewb
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  • Last checkedFeb. 26th '22
  • Date uploadedFeb. 24th '22
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Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions



https://CourseBoat.com

LinkedIn Learning
Duration: 2h 9m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 360 MB
Genre: eLearning | Language: English

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.

Files:

[ CourseBoat.com ] Linkedin - Machine Learning and AI Foundations - Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 001. Exploring the world of explainable AI and inte.en.srt (1.5 KB)
    • 001. Exploring the world of explainable AI and inte.mp4 (5.0 MB)
    • 002. Target audience.en.srt (2.0 KB)
    • 002. Target audience.mp4 (3.0 MB)
    • 003. What you should know.en.srt (1.5 KB)
    • 003. What you should know.mp4 (2.3 MB)
    2. What Are XAI and IML
    • 004. Understanding the what and why your mo.en.srt (6.6 KB)
    • 004. Understanding the what and why your mo.mp4 (16.4 MB)
    • 005. Variable importance and reason codes.en.srt (3.3 KB)
    • 005. Variable importance and reason codes.mp4 (9.2 MB)
    • 006. Comparing IML and XAI.en.srt (6.4 KB)
    • 006. Comparing IML and XAI.mp4 (10.5 MB)
    • 007. Trends in AI making the XAI problem mo.en.srt (8.0 KB)
    • 007. Trends in AI making the XAI problem mo.mp4 (18.3 MB)
    • 008. Local and global explanations.en.srt (3.5 KB)
    • 008. Local and global explanations.mp4 (5.3 MB)
    • 009. XAI for debugging models.en.srt (3.4 KB)
    • 009. XAI for debugging models.mp4 (7.0 MB)
    • 010. KNIME support of global and local expl.en.srt (3.4 KB)
    • 010. KNIME support of global and local expl.mp4 (5.4 MB)
    3. Why Isolating a Variable’s Contribution Is Difficult
    • 011. Challe.en.srt (13.0 KB)
    • 011. Challe.mp4 (23.1 MB)
    • 012. Challe.en.srt (5.0 KB)
    • 012. Challe.mp4 (7.8 MB)
    • 013. Rashom.en.srt (6.9 KB)
    • 013. Rashom.mp4 (11.7 MB)
    4. Black Box Model 101
    • 014. What qualifies as a black box.en.srt (4.3 KB)
    • 014. What qualifies as a black box.mp4 (7.8 MB)
    • 015. Why do we have black box models.en.srt (6.5 KB)
    • 015. Why do we have black box models.mp4 (9.7 MB)
    • 016. What is the accuracy interpretability t.en.srt (6.3 KB)
    • 016. What is the accuracy interpretability t.mp4 (10.6 MB)
    • 017. The argument against XAI.en.srt (4.5 KB)
    • 017. The argument against XAI.mp4 (7.1 MB)
    5. Introduction to KNIME for XAI and IML
    • 018. Introducing KNIME.en.srt (5.5 KB)
    • 018. Introducing KNIME.mp4 (14.5 MB)
    • 019. Building models in KN.en.srt (8.1 KB)
    • 019. Building models in KN.mp4 (14.6 MB)
    • 020. Understanding looping.en.srt (4.3 KB)
    • 020. Understanding looping.mp4 (9.8 MB)
    • 021. Where to find availab.en.srt (3.6 KB)
    • 021. Where to find availab.mp4 (12.1 MB)
    6. XAI Techniques Global Explanations
    • 022. Providing global explana.en.srt (6.6 KB)
    • 022. Providing global explana.mp4 (12.3 MB)
    • 023. Using surrogate models f.en.srt (2.6 KB)
    • 023. Using surrogate models f.mp4 (5.9 MB)
    • 024. Developing and interpret.en.srt (6.5 KB)
    • 024. Developing and interpret.mp4 (12.8 MB)
    • 025. Permutation feature impo.en.srt (1.5 KB)
    • 025. Permutation feature impo.mp4 (4.6 MB)
    • 026. Global feature importanc.en.srt (10.4 KB)
    • 026. Global feature importanc.mp4 (21.1 MB)
    7. Techniques for Local Explanations
    • 027. Developing an intuition f.en.srt (6.8 KB)
    • 027. Developing an intuition f.mp4 (10.0 MB)
    • 028. Introducing SHAP.en.srt (2.7 KB)
    • 028. Introducing SHAP.mp4 (3.6 MB)
    • 029. Using LIME to provide loc.en.srt (3.1 KB)
    • 029. Using LIME to provide loc.mp4 (5.2 MB)
    • 030. What are counterfactuals.en.srt (3.8 KB)
    • 030. What are counterfactuals.mp4 (5.7 MB)
    • 031. KNIME's Local Explanation.en.srt (6.0 KB)
    • 031. KNIME's Local Explanation.mp4 (11.6 MB)
    • 032. XAI View node demonstrati.en.srt (9.9 KB)
    • 032. XAI View node demonstrati.mp4 (13.8 MB)
    8. IML Techniques
    • 033. General advice for better IML.en.srt (6.8 KB)
    • 033. General advice for better IML.mp4 (14.6 MB)
    • 034. Why feature engineering is critical for IML.en.srt (3.3 KB)
    • 034. Why feature engineering is critical for IML.mp4 (9.2 MB)
    • 035. CORELS and recent trends.en.srt (7.5 KB)
    • 035. CORELS and recent trends.mp4 (11.5 MB)
    9. Conclusion
    • 036. Continuing to explore XAI.en.srt (2.2 KB)
    • 036. Continuing to explore XAI.mp4 (3.2 MB)
    • Bonus Resources.txt (0.4 KB)
      • Ex_Files_ML_and_AI_Foundations_Explainable_Interpretable Exercise Files 02_01
      • auto-mpg_hp_tons.xlsx (40.8 KB)
      05_01
      • 05_01_XAI_PDP.knar.knwf (2.1 MB)
      05_03
      • 05_03_XAI_Surrogates.knar.knwf (2.0 MB)

Code:

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