{"id":526,"date":"2025-05-27T06:00:36","date_gmt":"2025-05-27T06:00:36","guid":{"rendered":"https:\/\/academy.xebia.com\/in\/?post_type=course&p=526"},"modified":"2026-07-28T11:27:00","modified_gmt":"2026-07-28T11:27:00","slug":"machine-learning-explainability","status":"publish","type":"course","link":"https:\/\/academy.xebia.com\/in\/training\/machine-learning-explainability\/","title":{"rendered":"Machine Learning Explainability\u202f"},"content":{"rendered":"\n
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\n\t\t\t\t\n\tMachine Learning Explainability<\/strong><\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t
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\n\tThis one-day training course will help prevent your models from being a black box. It will provide you with a toolbox of Machine-Learning explainability techniques that you can use to explain your models to technical and non-technical people alike. You will also learn when, how, and why you should use the different techniques, and their drawbacks.<\/p>\t\t\t\t

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\n\t\t\n\tLooking to upskill your team(s) or organization?<\/strong><\/h2>

\n\tSurat will gladly help you further with custom training solutions.<\/p>

\n\t\t\n\n\t\n\t\n\t\n<\/picture>\t\t\n\tSurat Wahal
Training Advisor<\/h2>\t\t

\n\t+91-9999644790
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linkedin.com\/in\/surat-wahal-12996513<\/a>
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\n\tAfter the training, you will be able to:<\/p>\t

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\n\tExplain the use cases for model explainability<\/p><\/div>\n\n\n\n

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\n\tEvaluate when model explainability is not enough (correlation vs. causality, fairness)<\/p><\/div>\n\n\n\n

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\n\tCategorize the used methods into sensitivity vs. impact as well as explaining single predictions (local explainability) vs. multiple predictions (global explainability)<\/p><\/div>\n\n\n\n

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\n\tApply the explainability methods with the provided Python packages<\/p><\/div>\n\n\n\n

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\n\tSummarize the advantages and disadvantages for each method<\/p><\/div>\n\n\n\n

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\n\tEvaluate whether a method is appropriate for the business use case<\/p><\/div>\n\n\t<\/div>\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n

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