I am starting with a data science interview series to check your knowledge, let's start with the first question. Here it is:

Question 1:
Explain the difference between supervised and unsupervised learning.

Let me know answer in comments 👇👇

Question 2:
What is overfitting in machine learning, and how can you prevent it?

Question 3:
What is the bias-variance tradeoff in machine learning?

Question 4:
What are some common techniques to handle missing data in a dataset?

5 DataAnalytics Project Ideas to boost your resume:

1. Stock Market Portfolio Optimization

2. YouTube Data Collection & Analysis

3. Elections Ad Spending & Voting Patterns Analysis

4. EV Market Size Analysis

5. Metro Operations Optimization

Question 5:
Explain the concept of a confusion matrix. What are precision, recall, and F1-score?

Question 6:
What is cross-validation, and why is it important in machine learning?

Question 7:
Can you explain the difference between a parametric and a non-parametric model?

What type of project do you enjoy working on the most?

1. Personal projects
2. Open-source contributions
3. Freelance work
4. Corporate projects
5. Academic projects

If any other, add in comments 👇👇

Question 8:
What is feature engineering, and why is it important in building machine learning models?

Happy Independence Day guys 🇮🇳