1. What Are the Different Types of Machine Learning?
  2. What Is Overfitting, and How Can You Avoid It?
  3. What Is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
  4. How Do You Handle Missing or Corrupted Data in a Dataset?
  5. How Can You Choose a Classifier Based on a Training Set Data Size?
  6. Explain the Confusion Matrix with Respect to Machine Learning Algorithms.
  7. What Is a False Positive and False Negative and How Are They Significant?
  8. What Are the Three Stages of Building a Model in Machine Learning?
  9. What Is Deep Learning?
  10. What Are the Differences Between Machine Learning and Deep Learning?
  11. What Are the Applications of Supervised Machine Learning in Modern Businesses?
  12. What Is Semi-supervised Machine Learning?
  13. What Are Unsupervised Machine Learning Techniques?
  14. What Is the Difference Between Supervised and Unsupervised Machine Learning?
  15. Compare K-means and KNN Algorithms.

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