First Round :
It’s complete technical interview round focusing on data science, machine learning, and deep learning. This round typically lasts for about 1.5 hours and includes a mix of theoretical questions, practical problem-solving exercises, and possibly coding challenges. Here are some questions :
- What is overfitting in machine learning? How can it be prevented or mitigated?
- Describe the bias-variance tradeoff and its implications for model performance.
- Explain the architecture and components of a convolutional neural network (CNN). How are CNNs used in image recognition tasks?
- Explain the concept of batch normalization and its significance in training deep neural networks.
- Define evaluation metrics commonly used in classification tasks, such as accuracy, precision, recall, F1-score, and ROC-AUC. When would you use each of these metrics?
- Describe the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. How do they help in evaluating binary classification models, especially when dealing with imbalanced datasets?
- Some basic pandas related question in python coding.
- string and list related questions
Second Round :
This round they completed focused the my current project aiming to gain a deep understanding of their practical experience, problem-solving abilities, and domain expertise. Here’s a more detailed breakdown :
Project Overview
- Architecture and Technologies Used
- Data Collection and Preprocessing
- Modeling Approach
- Challenges Faced
Third Round:
The manager round, the focus shifts from technical skills to assessing company culture:
- Casual Conversation
- Company/Product Overview
- Role Expectations
- Behavioral Questions
- Career Goals
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