Skip to main content

Extension

Instruction and application
Complete

Why keep learning?​

Illustration

The world of AI is constantly evolving.

The ongoing emergence of novel architectures, optimization strategies, and hardware advancements presents significant opportunities for enhanced model performance, efficiency gains, and the development of more sustainable AI practices.

By actively engaging with these evolving concepts, you can refine your skills, implement state-of-the-art techniques, and ultimately drive more impactful and innovative solutions within your organization.

Dive deeper: Additional learning materials​

If you're interested, use the following resources to continue exploring topics related to this unit.

Machine learning performance metrics

  • This is a module from Google's Machine Learning Crash Course. It provides a clear explanation of key classification metrics like accuracy, precision, recall, and the F1-score. It also discusses the trade-offs between these metrics and their relevance for different types of problems and datasets, including imbalanced ones.

  • This article from Analytics Vidhya offers a comprehensive overview of 12 important model evaluation metrics for machine learning, covering both classification and regression tasks. It explains each metric with formulas and provides insights into when to use them. Metrics discussed include accuracy, precision, recall, F1-score, ROC-AUC, Mean Squared Error, and more. Model optimization techniques

  • This is the official guide to the TensorFlow Model Optimization Toolkit. It explains various techniques to optimize machine learning models for inference, focusing on reducing latency, memory usage, and power consumption. Techniques covered include quantization, sparsity and pruning, clustering, and collaborative optimization.

  • This Netguru blog post discusses AI model optimization techniques for enhanced performance in 2025. It covers parameter optimization (regularization, pruning, quantization), hyperparameter optimization, transfer learning, and provides insights into evaluating and comparing different optimization methods.

Ensemble methods

  • This is the ensemble methods section from the scikit-learn documentation. It provides a detailed explanation of various ensemble techniques like bagging (including Random Forests and Extra Trees), boosting (including Gradient Boosting and AdaBoost), and voting/stacking classifiers and regressors, along with their implementation details and parameters in scikit-learn.

  • This IBM article provides a good overview of ensemble learning, explaining why it's used and the concepts of bias-variance tradeoff. It describes different types of ensemble methods, including bagging, boosting, and stacking, and discusses the importance of diversity among the combined models.