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I applied via Campus Placement and was interviewed before Jul 2023. There were 2 interview rounds.
Questions on Prob Stats, ML
I was a test in our college of about 45min revolving around aptitude.
Few basic coding questions.
Common ways to evaluate Time Series model include AIC, BIC, RMSE, MAE, ACF, PACF, etc.
Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare models
Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to assess model accuracy
Analyze Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to check for autocorrelation in residuals
Use techniques like feature selection, regularization, PCA, and VIF to handle multicollinearity.
Perform feature selection to choose the most relevant variables for the model.
Apply regularization techniques like Lasso or Ridge regression to penalize high coefficients.
Utilize Principal Component Analysis (PCA) to reduce dimensionality and decorrelate variables.
Check for Variance Inflation Factor (VIF) to identify highly
TF IDF is a technique used in NLP to measure the importance of a word in a document within a collection of documents.
TF IDF stands for Term Frequency-Inverse Document Frequency.
It is used to determine how important a word is in a document relative to a collection of documents.
TF IDF is calculated by multiplying the term frequency (TF) of a word in a document by the inverse document frequency (IDF) of the word across al...
I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.
The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.
Normal aptitude questions
I applied via Job Portal and was interviewed in Dec 2021. There were 2 interview rounds.
I applied via Naukri.com and was interviewed in Mar 2024. There were 3 interview rounds.
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.
Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.
These algorithms require training data to learn patte...
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
1. Define the problem and objectives of the credit risk model.
2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.
3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.
4. Select a suitable machine learning algorithm such as logi...
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.
BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
XGBoost is known for its accuracy and performance on structured/tabular data.
LightGBM is faster and more memory-efficient, making it suitable for large datasets.
LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
I applied via Naukri.com and was interviewed before May 2023. There were 2 interview rounds.
Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test
I applied via Campus Placement and was interviewed in Oct 2024. There were 3 interview rounds.
Asked questions of finance and aptitude
Machine learning is a branch of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data.
Machine learning is a subset of artificial intelligence that focuses on developing algorithms that can learn from and make predictions or decisions based on data.
It involves training models on large datasets to recognize patterns and m...
SQL is a programming language used for managing and manipulating relational databases.
SQL stands for Structured Query Language
It is used to retrieve and manipulate data in relational databases
Common SQL commands include SELECT, INSERT, UPDATE, DELETE
SQL can be used to create tables, indexes, and views
Examples of SQL databases include MySQL, PostgreSQL, Oracle
Software development involves creating, designing, testing, and maintaining software applications.
Software development includes coding, testing, debugging, and documenting software applications.
Developers use programming languages like Java, Python, C++, etc. to write code.
Agile and Waterfall are common software development methodologies.
Version control systems like Git are used to manage code changes.
Software developm...
Various machine learning algorithms with brief details
Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest
Unsupervised Learning: K-means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA)
Reinforcement Learning: Q-Learning, Deep Q-Networks (DQN)
Neural Networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), L
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