Introduction to Machine Learning Models (AI) Testing
- Descrição
- Currículo
- FAQ
- Revisões
This course will introduce you to the World of Machine Learning Models Testing.
As AI continues to revolutionize industries, many companies are developing their own ML models to enhance their business operations. However, testing these models presents unique challenges that differ from traditional software testing. Machine Learning Model testing requires a deeper understanding of both data quality and model behavior, as well as the algorithms that power them.
This Course starts with explaining the fundamentals of the Artificial Intelligence & Machine Learning concepts and gets deep dive into testing concepts & Strategies for Machine Learning models with real time examples.
Below is high level of Agenda of the tutorial:
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Introduction to Artificial Intelligence
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Overview of Machine Learning Models and their Lifecycle
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Shift-Left Testing in the ML Engineering Phase
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QA Functional Testing in the ML Validation Phase
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API Testing Scope for Machine Learning Models
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Responsible AI Testing for ML Models
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Post-Deployment Testing Strategies for ML Models
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Continuous Tracking and Monitoring Activities for QA in Production
By the end of this course,
you will gain expertise in testing Machine Learning Models at every stage of their lifecycle.
Please Note:
This course highlights specialized testing types and methodologies unique to Machine Learning Testing, with real-world examples.
No specific programming language or code is involved in this tutorial.
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1Introduction and Agenda of the tutorialVídeo Aula
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2Introduction to Artificial Intelligence Systems with examplesVídeo Aula
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3What is Machine Learning and how it is related to Artificial Intelligence familyVídeo Aula
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4Examples of commonly used Machine Learning Models and their usageVídeo Aula
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5Material downloadTexto
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6Understand Machine Learning Model Life cycle stages with online/offline modesVídeo Aula
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7How Machine Learning models works in nutshell -Learn terminologies usedVídeo Aula
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8Understand how OverFitting Testng & UnderFitting works with Trained data setsVídeo Aula
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9Predicting House Prices (ML Model) Demo to show how internally Algorithms worksVídeo Aula
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10Revision on Supervised Learning Model Testing with Overfitting/UnderFitting exVídeo Aula
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11Material downloadTexto
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12Knowledge Check - Section-2Questionário
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13Introduction to Unsupervised Learning in the ML models with exampleVídeo Aula
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14Testing scope on Unsupervised Learning with Data point patterns&Cluster scoresVídeo Aula
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15Revision on Unsupervised Learning with cluster score analysisVídeo Aula
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16Material downloadTexto
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17Knowledge Check - Section-3Questionário
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22What are Validation Unseen Data sets and why it is requiredVídeo Aula
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23Temperature Testing to fine tune the response predictions from ML ModelsVídeo Aula
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24Prompts Testing with Zero Shot & Chain of thought Prompts testVídeo Aula
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25Relevance stary Testing & Fantasy claims testing on ML ModelsVídeo Aula
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26Repeatability Testing & Asking question in different phases to testVídeo Aula
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27Style Transfer testing & Intent recognition testing on ML ModelsVídeo Aula
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28What is Invariance Testing & BiDirectional testing for AI ModelsVídeo Aula
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29Material downloadTexto
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30Knowledge Check - Section-5Questionário
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34Importance of Fairness testing on ML responses to check biasVídeo Aula
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35Transparency testing and why it is necessary to stay ahead in AI competitionVídeo Aula
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36Data Privacy and Security testing on Machine Learning modelsVídeo Aula
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37Material downloadTexto
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38Knowledge Check: Section-7Questionário
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39Importance of Integration & Latency testing on Production ML modelsVídeo Aula
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40Importance of Data drift Testing & Concept Drift Testing in ML ModelsVídeo Aula
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41Shadow Testing & A/B Testing to certify the latest version of ML into prodVídeo Aula
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42Material downloadTexto
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43Knowledge Check: Section-8Questionário
