In this two‑week course, you’ll dive into core machine learning modeling techniques and real‑world system design.
- Week 1 covers debugging model development and exploring model‑ vs. data‑centric improvement approaches, helping you build solid baselines.
- Week 2 introduces foundation models and PyTorch fundamentals, equipping you to leverage modern architectures and frameworks for advanced ML tasks.
By mastering hyperparameter tuning, ensemble methods, feature engineering and diagnosing overfitting, underfitting and class imbalance, you’ll learn to optimize model performance end to end. Ethical considerations and best practices for data preparation and labeling ensure you can deploy reliable, fair and effective ML solutions—skills that directly accelerate project delivery and elevate your professional impact.