1. Discuss your experience with genAI and ML projects.
During the interview, you will be asked about your experience with genAI and ML projects. You should be prepared to discuss the projects you have worked on, the technologies and tools you used, and any challenges you faced. Additionally, be ready to answer follow-up questions such as whether you used open-source code and how familiar you are with the codebase. Another potential follow-up could involve discussing instances where the model's performance did not reflect the actual results.
2. Implement K-means from Scratch
Implement K-means clustering algorithm from scratch. Additionally, address the following related questions: How would you initialize clusters? How would you choose the number of clusters? How can K-means be made efficient when dealing with large amounts of data?
3. Design a Text NLP Tool
How would you design a text NLP (Natural Language Processing) tool?
4. Explain the concept of k-means clustering and implement it.
During the interview, you are asked to explain the concept of k-means clustering. After explaining, you are then tasked with implementing the algorithm. Due to time constraints, you are also asked to discuss how you would proceed further if given more time.
5. Reinforcement Learning in Recommendation Systems
Discuss the application of Reinforcement Learning (RL) in recommendation systems.
