Learn Python by Building Data Science Applications

Questions

1. What is machine learning?
2. What is the difference between supervised and unsupervised learning?
3. What are the drawbacks of k-means clustering? Why do we need to use a scaler?
4. How does the KNN model work? What are the benefits and limitations of such a model?
5. Why does linear regression give more interpretations? Do we need to scale data in this case?
6. How do decision trees work compared to other models we described?