Modelicon Infotech LLP Data Scientist Intern Interview Questions and Answers for Freshers

Q1. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q2. Parameters of Decision Tree
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Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
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Q3. Explain any one of your project in detail
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Developed a predictive model to forecast customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to the company to reduce customer churn rate
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Q1. Project related questions from your CV
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Q1. Question on transformers
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Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
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Q1. ML and deep learning questions
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Q1. Projects discussion
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Q2. Chatgpt architecture
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Basic DSA questions will be asked Leetcode Easy to medium

Q1. BERT vs LSTM and their speed
Ans. 
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
• BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.

• BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.

• For exa...read more
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Q2. What is multinomial Naive Bayes theorem
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Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
• It is commonly used in text classification tasks, such as spam detection or sentiment analysis.

• It is suitable for features that represent counts or frequencies, like word counts in text data.

• It calculates the probability of each class given the input features and selects the class with the
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Q1. What is L1 and L2 Regularization?
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L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding penalty terms to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term to the cost function.

• L2 regularization adds the squared values of the coefficients as penalty term to the cost function.

• L1 regularization can lead to sparse models by forcing some coefficients to be exactly zer...read more
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Q1. What are hyperparameters in random forest
Ans. 
Hyperparameters in random forest are parameters that are set before the learning process begins.
• Hyperparameters control the behavior of the random forest algorithm.

• They are set by the data scientist and are not learned from the data.

• Examples of hyperparameters in random forest include the number of trees, the maximum depth of trees, and the number of features considered at each split.
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Q2. How to do QnA system with LLM
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A QnA system with LLM is a system that uses the Language Model for Information Retrieval and Question Answering.
• Preprocess the input question and convert it into a format suitable for the LLM model.

• Fine-tune the LLM model on a dataset of question-answer pairs.

• Use the fine-tuned model to generate answers for new questions.

• Evaluate the performance of the QnA system using metrics like precision, recall, and F1 score.

• Itera...read more
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Q3. How to do unit testing
Ans. 
Unit testing is a process of testing individual units of code to ensure they function correctly.
• Write test cases for each unit of code

• Test inputs, outputs, and edge cases

• Use testing frameworks like JUnit or pytest

• Automate tests to run regularly

• Ensure tests are independent, isolated, and repeatable
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Question related to maths basic and some basic blood relations questions

Q1. All the questions we asked based on the Resume
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Q2. Basic question of program like pattern matching prime number
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