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In this section, you learned the critical importance of feature engineering for AI models, emphasizing

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the process of transforming raw data into meaningful inputs that can significantly enhance model performance.

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You explored various techniques for creating, selecting, and scaling features to improve accuracy

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and efficiency.

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Moving on to model training, you delved into essential techniques and best practices, including data

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pre-processing, hyperparameter tuning, and the use of different optimization algorithms to achieve

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robust model training outcomes in model testing and validation processes.

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The focus was on ensuring the reliability and validity of AI models through cross validation, performance

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metrics, and the identification of overfitting or underfitting issues.

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You discovered methods for testing AI models with edge cases and adversarial inputs to evaluate their

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robustness and resilience against unexpected or malicious data.

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highlighting the importance of thorough testing in real world applications.

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Privacy preserving machine learning techniques were discussed to address the growing concerns of data

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privacy and security.

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You learned about differential privacy, federated learning, and other approaches to protect sensitive

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information while still enabling effective model training.

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Repeatability assessments and model fact sheets were introduced to standardize the documentation and

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evaluation of AI models, ensuring transparency and consistency in model development and deployment.

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Conducting algorithm impact assessments was another key topic where you gained insights into evaluating

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the broader implications of AI models, including ethical considerations, social impact, and potential

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biases.

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This section equipped you with the knowledge and skills necessary to create, train, test, and evaluate

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AI models responsibly and effectively, ensuring that they are not only high performing, but also ethical

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and reliable.