Learn about the common mistakes you should avoid when building your machine learning models.
Overview
Syllabus
Introduction
- Avoiding machine learning mistakes
- Using the exercise files
- Assuming data is good to go
- Neglecting to consult subject matter experts
- Overfitting your models
- Not standardizing your data
- Focusing on the wrong factors
- Data leakage
- Forgetting traditional statistics tools
- Assuming deployment is a breeze
- Assuming machine learning is the answer
- Developing in a silo
- Not treating for imbalanced sampling
- Interpreting your coefficients without properly treating for multicollinearity
- Evaluating by accuracy alone
- Giving overly technical presentations
- Take your machine learning skills to the next level
Taught by
Brett Vanderblock and Madecraft
