Question 6:
What is cross-validation, and why is it important in machine learning?
What is cross-validation, and why is it important in machine learning?
👍8
Question 7:
Can you explain the difference between a parametric and a non-parametric model?
Can you explain the difference between a parametric and a non-parametric model?
👌11👍4
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 👇👇
1. Personal projects
2. Open-source contributions
3. Freelance work
4. Corporate projects
5. Academic projects
If any other, add in comments 👇👇
👍11
Question 8:
What is feature engineering, and why is it important in building machine learning models?
What is feature engineering, and why is it important in building machine learning models?
👍7❤2
Question 9:
What is the purpose of regularization in machine learning, and what are some common types of regularization techniques?
What is the purpose of regularization in machine learning, and what are some common types of regularization techniques?
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Add more in comments 👇👇
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Add more in comments 👇👇
👍14❤1
Question 10:
What is the difference between bagging and boosting in ensemble methods?
What is the difference between bagging and boosting in ensemble methods?
👍1
Question 11:
How do you select the right evaluation metric for a given machine learning problem?
How do you select the right evaluation metric for a given machine learning problem?
Question 12:
Can you walk me through a machine learning project you’ve worked on? What challenges did you face, and how did you overcome them?
Can you walk me through a machine learning project you’ve worked on? What challenges did you face, and how did you overcome them?
👍4
Data Science Projects
Struggle of a data scientist
What's the most struggling part while learning data science as per your experience?
❤6
Question 13:
How do you handle categorical variables in a dataset? What techniques do you use for encoding them?
How do you handle categorical variables in a dataset? What techniques do you use for encoding them?
👍9
Question 14:
Explain the difference between a ROC curve and a Precision-Recall curve. When would you use one over the other?
Explain the difference between a ROC curve and a Precision-Recall curve. When would you use one over the other?
Question 15:
How do you deal with multicollinearity in regression models? What methods can be used to detect and address it?
How do you deal with multicollinearity in regression models? What methods can be used to detect and address it?
Question 16:
What is the difference between hard and soft clustering? Can you give an example of algorithms that use each approach?
What is the difference between hard and soft clustering? Can you give an example of algorithms that use each approach?
👍2