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

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Linkedin - Machine Learning and AI Foundations - Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions (Size: 360.5 MB)
  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 MB
  002. Target audience.en.srt 2 KB
  002. Target audience.mp4 3 MB
  003. What you should know.en.srt 1.5 KB
  003. What you should know.mp4 2.3 MB
  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 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 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
  011. Challe.en.srt 13 KB
  011. Challe.mp4 23.1 MB
  012. Challe.en.srt 5 KB
  012. Challe.mp4 7.8 MB
  013. Rashom.en.srt 6.9 KB
  013. Rashom.mp4 11.7 MB
  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
  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
  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
  027. Developing an intuition f.en.srt 6.8 KB
  027. Developing an intuition f.mp4 10 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 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
  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
  036. Continuing to explore XAI.en.srt 2.2 KB
  036. Continuing to explore XAI.mp4 3.2 MB
  05_01_XAI_PDP.knar.knwf 2.1 MB
  05_03_XAI_Surrogates.knar.knwf 2 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  auto-mpg_hp_tons.xlsx 40.8 KB
  ▲ 77 total files

Description


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.

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