Completed Google Course: The Nuts and Bolts of Machine Learning
I’m proud to share that I’ve successfully completed the The Nuts and Bolts of Machine Learning course, authorized by Google and offered through Coursera.
Machine Learning is not just about using models — it’s about understanding how they are built, evaluated, optimized, and deployed. This course focused on the practical foundations required to design reliable, scalable ML systems.
This program strengthened my ability to approach ML problems systematically, from data preparation to model evaluation and performance improvement.
Throughout the course, I reinforced key ML-relevant competencies, including:
- Supervised learning foundations: understanding classification and regression workflows.
- Model training & evaluation: applying validation strategies and performance metrics effectively.
- Overfitting & generalization control: interpreting bias–variance tradeoffs and improving robustness.
- Feature engineering principles: preparing structured data for predictive modeling.
- Model comparison & selection: choosing appropriate algorithms based on problem constraints and performance behavior.
This course reinforced the engineering mindset behind Machine Learning — building models that are not only accurate, but statistically sound, interpretable, and production-ready.