Section 3
ML Engineering in Practice
Sections 1 and 2 answered "how does this algorithm work." Section 3 answers the question every one of those chapters deferred: what happens when the data isn't clean, the model overfits, a stakeholder needs an explanation, and the whole thing has to run in production instead of a notebook.
Chapters
7
Lesson Reading
3 hr 35 min
Labs
7
Interview Sets
7
Chapter Path
Chapter 13Regularization25 minutesAcademy PassChapter 14Hyperparameter Tuning & Cross-Validation25 minutesAcademy PassChapter 15Handling Imbalanced & Messy Real-World Data25 minutesAcademy PassChapter 16Model Interpretability35 minutesAcademy PassChapter 17ML Pipelines in Production35 minutesAcademy PassChapter 18Where Classical ML Ends and Deep Learning Begins35 minutesAcademy PassChapter 19End of Volume Review & Interview Questions35 minutesAcademy Pass