Mistakes to Avoid in Machine Learning

Learn about the common mistakes you should avoid when building your machine learning models.

Introduction

• Avoiding machine learning mistakes
• Using the exercise files

1. Mistakes to Avoid

• 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

Conclusion

• Take your machine learning skills to the next level