End-to-End Machine Learning Project

Challenge yourself with a quiz covering preprocessing,modeling,and deployment workflows.

1. What is the first step in an end-to-end machine learning project?
2. Which of the following are key components of data preprocessing? (Select all that apply)
3. Exploratory Data Analysis (EDA) is primarily used to visualize model performance.
4. What does EDA stand for in the context of machine learning projects?
5. Which metric is most appropriate for evaluating a regression model?
6. Which of the following are common model deployment platforms? (Select all that apply)
7. Data preprocessing is optional in end-to-end ML projects if the data is 'clean'.
8. Name the process of creating new input variables from raw data to improve model performance.
9. Which phase involves selecting the best algorithm after initial training?
10. What are key aspects of problem scoping? (Select all that apply)
11. Supervised learning projects require labeled data for model training.
12. What does MLOps stand for in the context of ML projects?
13. Which metric is best for evaluating imbalanced classification datasets?
14. Which steps are part of model evaluation? (Select all that apply)
15. Model monitoring ends once a model is deployed to production.
16. What technique adjusts model parameters (e.g., learning rate) to optimize performance without retraining?
17. Which tool is used for versioning data and models in ML projects?
18. Which are common data sources for ML projects? (Select all that apply)
19. EDA (Exploratory Data Analysis) includes visualizing data distributions and relationships.
20. Name the final step where a trained model is made available for real-world use.
Answered 0 of 0 — 0 correct