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# Resources for Self-Study - AI Expert Certification on Training, Evaluating, and Deploying ML Models
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## Overview
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The ***AI Expert Certification on Training and Evaluating ML Models*** validates knowledge on fundamental machine learning concepts, including the training of deep neural networks.
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Candidates must first pass a knowledge test before being eligible to submit a portfolio project developed in Python. See the details and FAQ on the [certification page]().
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Sample questions from the knowledge test [are available here]().
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The rubric used to evaluate portfolio projects is [available here](). Projects are to be submitted using Python as the main programming language.
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Note that candidates must present their portfolio projects in a live interview where both their presentation skills and their code are evaluated.
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We have compiled the following resources to provide guidance on the theoretical and practical topics evaluated in the certification.
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# Resources for Self-Study
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The content on this guide touches concepts that we go through on our AI Maker Sessions.
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## Practical Courses on Kaggle Learn
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*[What is CRISP-DM?](https://www.datascience-pm.com/crisp-dm-2/)
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## Self-Study Content on the No-Free-Lunch Theorems
*[Lack of A-Priori Distinction on Learning Algorithms by David Wolpert](https://www.researchgate.net/publication/2755783_The_Lack_of_A_Priori_Distinctions_Between_Learning_Algorithms)
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*[An Overview of the No-Free-Lunch Theorems for Machine Learning](https://machinelearningmastery.com/no-free-lunch-theorem-for-machine-learning/)
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