• REVIEW: ML Models Pros and Cons
• REVIEW: Model Training Terminology
• 1. What is the Independence Assumption for a Naive Bayes Classifier?
• 2. Explain the Linear Regression Model and Discuss Its Assumption?
• 3. Explain Briefly the K-Means Clustering and How Can We Find the Best Value of K?
• 4. Explain What is Information Gain and Entropy in the Context of Decision Trees?
• 5. Mention Three Ways to Handle Missing or Corrupted Data in a dataset?
• 6. Strategies for Mitigating the Impact of Outliers in Model Training
• 7. Explain Briefly the Logistic Regression Model and State an Example of When You Have Used It Recently?
• 8. Describe Briefly the Hypothesis Testing and P-value in Layman’s Terms? and Give a Practical Application for Them?
• 9. What is an Activation Function and Discuss the Use of an Activation Function? Explain Three Different Types of Activation Functions?
• REVIEW: Dimensionality Reduction Techniques
• 10. What Do You Do When You Have a Low Amount of Data and Large Amount of Features?
• REVIEW: Define Correlation
• 11. What is a Correlation Coefficient?
• 12. Explain Pearson’s Correlation Coefficient.
• 13. Explain Spearman’s Correlation Coefficient.
• 14. Compare Pearson and Spearman Coefficients.
• REVIEW: Multicollinearity
• 16. What are L1 and L2 Regularization? What are the Differences Between the Two?