Read on Twitter
Here are 20 fundamental questions that you need to ace before getting a Machine Learning job.
Almost every company will ask these to weed out non-prepared candidates. You don't want to show up unless you are comfortable having a discussion about all of these.
Almost every company will ask these to weed out non-prepared candidates. You don't want to show up unless you are comfortable having a discussion about all of these.
Of course, this is not an exhaustive list. There are many more topics and concepts you should master before applying for a job.
But hopefully, these will give you an idea of where you stand today.
Let's get started!
But hopefully, these will give you an idea of where you stand today.
Let's get started!
Warming up 1. Explain the difference between Supervised and Unsupervised methods.
2. What's your favorite algorithm? Can you explain how it works?
3. Given a specific dataset, how do you decide which is the best algorithm to use?
Getting one step deeper 4. When should you use classification over regression?
5. Can you explain how Logistic Regression works?
6. What are the advantages and disadvantages of decision trees?
7. Can you compare K-means with KNN?
This is about to get real! 8. How much data would you allocate for your training, validation, and test sets?
9. Can you explain what is the "Curse of Dimensionality"?
10. What are some methods to reduce dimensionality?
11. How would you handle an imbalanced dataset?
Let's now get deep into it! 12. Can you explain the trade-off between bias and variance?
13. Can you define and explain the differences between precision and recall?
14. How do you define the F1 score and why is it useful?
15. How do you ensure you're not overfitting? Can you explain some techniques to reduce overfitting?
16. Can you explain what is cross-validation and how is it useful?
17. Can you explain the difference between L1 and L2 regularization?
16. Can you explain what is cross-validation and how is it useful?
17. Can you explain the difference between L1 and L2 regularization?
18. What is the ROC Curve?
19. What is a Confusion Matrix and how is it useful?
20. Which is more important: model accuracy or model performance?
19. What is a Confusion Matrix and how is it useful?
20. Which is more important: model accuracy or model performance?
If you start practicing these questions, a whole universe of knowledge will open before you. It's fascinating!
In the coming days, I'll be posting the answers and more specific content related to each one of these questions.
I hope you are there to add to the conversation!
In the coming days, I'll be posting the answers and more specific content related to each one of these questions.
I hope you are there to add to the conversation!
