Top Mistakes Novice Machine Learning Engineers Make?

  1. Spending Less Time in Understanding the data and EDA.
  2. Not Communicating well.
  3. Spending more time on Theory without practical application.
  4. Focusing on Accuracy over Understanding how the model works.
  5. Giving Preference to Tools over Business problems.
  6. Ignoring Outliers.
  7. Using L1, L2 Regularization without Standardization.
  8. No proper understanding on how to transform categorical variables.
  9. Not picking the right loss function.
  10. Not Focusing on the Distribution of data.
  11. Correlation Does Not Imply Causation.
  12. Assuming the Algorithms are more important then Domain Knowledge.

Jr Data Scientist | AI researcher