Question 9:
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

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Question 10:
What is the difference between bagging and boosting in ensemble methods?

⌨️ Python Quiz

Question 11:
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?

Struggle of a data scientist

What's the most struggling part while learning data science as per your experience?

⌨️ Python Quiz

Question 13:
How do you handle categorical variables in a dataset? What techniques do you use for encoding them?

Question 14:
Explain the difference between a ROC curve and a Precision-Recall curve. When would you use one over the other?

✨ The answer is: 👇👇

Question 15:
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?

As a programmer, do you like mathematics?

Important metrics to monitor while monitoring machine learning model

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At the age of 19, 20, 21+ you will start to realize that life ain't easy. your circle of friends will start to become smaller. you lose yourself, you become frustrated, lonely for no reason, you will develop trust issues, you cry silently at night and wake up in the morning like nothing happened. you think about giving up many times but in the end you find yourself fighting again because you realize that this is stage where you must be strong to fight your fears and possibilities that everything will leave you.

Drop ❤️ if u felt this