KEY LEARNINGS
- Machine learning enables computers to improve through experience rather than explicit programming.
- Supervised learning uses labeled examples, unsupervised learning finds hidden patterns, and reinforcement learning optimizes through trial and error.
- The training process involves data collection, feature engineering, model training, and testing—each step can introduce errors.
- Model drift means trained models degrade over time as real-world patterns change.
- Effective governance requires continuous monitoring and clear criteria for model retraining or retirement.
- 🌐Google Machine Learning Crash CourseFree comprehensive introduction to machine learning concepts.
- 🎥Coursera: Machine Learning by Andrew NgStanford's foundational machine learning course.
- 🔧Scikit-learn DocumentationPractical ML library documentation with tutorials.
- Samuel, A. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development.
- Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.
- Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature.
