In this section, you learned the critical importance of feature engineering for AI models, emphasizing
the process of transforming raw data into meaningful inputs that can significantly enhance model performance.
You explored various techniques for creating, selecting, and scaling features to improve accuracy
and efficiency.
Moving on to model training, you delved into essential techniques and best practices, including data
pre-processing, hyperparameter tuning, and the use of different optimization algorithms to achieve
robust model training outcomes in model testing and validation processes.
The focus was on ensuring the reliability and validity of AI models through cross validation, performance
metrics, and the identification of overfitting or underfitting issues.
You discovered methods for testing AI models with edge cases and adversarial inputs to evaluate their
robustness and resilience against unexpected or malicious data.
highlighting the importance of thorough testing in real world applications.
Privacy preserving machine learning techniques were discussed to address the growing concerns of data
privacy and security.
You learned about differential privacy, federated learning, and other approaches to protect sensitive
information while still enabling effective model training.
Repeatability assessments and model fact sheets were introduced to standardize the documentation and
evaluation of AI models, ensuring transparency and consistency in model development and deployment.
Conducting algorithm impact assessments was another key topic where you gained insights into evaluating
the broader implications of AI models, including ethical considerations, social impact, and potential
biases.
This section equipped you with the knowledge and skills necessary to create, train, test, and evaluate
AI models responsibly and effectively, ensuring that they are not only high performing, but also ethical
and reliable.