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I am drawn to this organization for its commitment to excellence and the opportunity to contribute to a collaborative team environment.
I admire the company's values and mission, particularly its focus on community service and support.
The opportunity for professional growth and development aligns with my career goals, as I am eager to learn and advance.
I appreciate the positive work culture here, which fosters team...
Selecting k value in kmeans algorithm involves using techniques like elbow method and silhouette score.
Use the elbow method to find the point where the rate of decrease sharply shifts, indicating the optimal k value.
Calculate silhouette score for different k values and choose the one with the highest score.
Consider domain knowledge and the specific problem requirements when selecting k value.
Experiment with differ...
Eigen vector is a vector that does not change its direction when a linear transformation is applied to it.
Eigen vectors are used in linear algebra to understand the behavior of linear transformations.
They represent directions along which a linear transformation has a simple effect, such as scaling.
Eigen vectors are associated with eigenvalues, which represent the scaling factor of the eigenvector.
For example, in a...
Parametric types of machine learning are algorithms that make assumptions about the functional form of the relationship between inputs and outputs.
Parametric models have a fixed number of parameters that are learned from the training data.
Examples include linear regression, logistic regression, and linear SVM.
They are often simpler and faster to train compared to non-parametric models.
Parametric models are suitabl...
Train semi supervised machine learning models by using a combination of labeled and unlabeled data.
Start by training a model on a small amount of labeled data
Use the trained model to make predictions on the unlabeled data
Incorporate the predictions into the training set and retrain the model
Repeat the process until the model reaches a satisfactory level of performance
Precision and recall can be calculated using values from a confusion matrix.
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Where TP = True Positive, FP = False Positive, FN = False Negative
For an n x n confusion matrix, sum the values in each row and column to get TP, FP, and FN for each class
Evaluation metrics are used to measure the performance or effectiveness of a system, project, or process.
Evaluation metrics can include quantitative measures such as accuracy, precision, recall, F1 score, and AUC-ROC.
They can also include qualitative measures such as user satisfaction, usability, and user engagement.
Evaluation metrics help in assessing the success of a project or system and identifying areas for i...
Yes, a neural network can accept complex numbers as input.
Neural networks can be designed to accept complex numbers as input by using complex-valued weights and activations.
Complex-valued neural networks have been used in applications such as signal processing and image recognition.
Complex numbers can represent both magnitude and phase information, making them useful for certain types of data.
Complex-valued neural...
Eigen value is a scalar associated with a square matrix that represents how a transformation stretches or compresses space along its eigenvectors.
Eigen values are solutions to the characteristic equation det(A - λI) = 0, where A is the matrix, λ is the eigen value, and I is the identity matrix.
They represent the factor by which the eigenvector is scaled during the transformation.
Eigen values can be real or complex...
Types of Machine Learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and self-supervised learning.
Supervised Learning: The model is trained on labeled data.
Unsupervised Learning: The model is trained on unlabeled data.
Semi-Supervised Learning: A combination of labeled and unlabeled data is used for training.
Reinforcement Learning: The model learns through ...
I applied via Company Website and was interviewed in Mar 2024. There was 1 interview round.
Train semi supervised machine learning models by using a combination of labeled and unlabeled data.
Start by training a model on a small amount of labeled data
Use the trained model to make predictions on the unlabeled data
Incorporate the predictions into the training set and retrain the model
Repeat the process until the model reaches a satisfactory level of performance
Parametric types of machine learning are algorithms that make assumptions about the functional form of the relationship between inputs and outputs.
Parametric models have a fixed number of parameters that are learned from the training data.
Examples include linear regression, logistic regression, and linear SVM.
They are often simpler and faster to train compared to non-parametric models.
Parametric models are suitable for...
Types of Machine Learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and self-supervised learning.
Supervised Learning: The model is trained on labeled data.
Unsupervised Learning: The model is trained on unlabeled data.
Semi-Supervised Learning: A combination of labeled and unlabeled data is used for training.
Reinforcement Learning: The model learns through trial...
Eigen value is a scalar associated with a square matrix that represents how a transformation stretches or compresses space along its eigenvectors.
Eigen values are solutions to the characteristic equation det(A - λI) = 0, where A is the matrix, λ is the eigen value, and I is the identity matrix.
They represent the factor by which the eigenvector is scaled during the transformation.
Eigen values can be real or complex numb...
Eigen vector is a vector that does not change its direction when a linear transformation is applied to it.
Eigen vectors are used in linear algebra to understand the behavior of linear transformations.
They represent directions along which a linear transformation has a simple effect, such as scaling.
Eigen vectors are associated with eigenvalues, which represent the scaling factor of the eigenvector.
For example, in a 2x2 ...
PCA is a mathematical technique used for dimensionality reduction by finding the principal components of a dataset.
PCA involves calculating the eigenvectors and eigenvalues of the covariance matrix of the data.
The eigenvectors represent the directions of maximum variance in the data, while the eigenvalues indicate the amount of variance along each eigenvector.
The principal components are the eigenvectors corresponding ...
For a small dataset of 300 samples, a neural network with one hidden layer would be more suitable for better accuracy.
A neural network with one hidden layer is simpler and less prone to overfitting on a small dataset.
With a small dataset, a complex neural network with more hidden layers may lead to overfitting and poor generalization.
A neural network with one hidden layer can capture the basic patterns in the data effe...
Yes, a neural network can accept complex numbers as input.
Neural networks can be designed to accept complex numbers as input by using complex-valued weights and activations.
Complex-valued neural networks have been used in applications such as signal processing and image recognition.
Complex numbers can represent both magnitude and phase information, making them useful for certain types of data.
Complex-valued neural netw...
kmeans algorithm is a clustering algorithm that partitions data into k clusters based on similarity.
Divides data points into k clusters based on distance from centroid
Iteratively assigns data points to nearest centroid and updates centroids
Converges when centroids no longer change significantly
Commonly used in machine learning for clustering data points
Evaluation metrics are used to measure the performance or effectiveness of a system, project, or process.
Evaluation metrics can include quantitative measures such as accuracy, precision, recall, F1 score, and AUC-ROC.
They can also include qualitative measures such as user satisfaction, usability, and user engagement.
Evaluation metrics help in assessing the success of a project or system and identifying areas for improv...
Machine learning is a subset of artificial intelligence that focuses on developing algorithms to make predictions based on data, while deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data.
Machine learning involves developing algorithms that can learn from and make predictions or decisions based on data.
Deep learning is a subset of machine learning that uses neural ...
Selecting k value in kmeans algorithm involves using techniques like elbow method and silhouette score.
Use the elbow method to find the point where the rate of decrease sharply shifts, indicating the optimal k value.
Calculate silhouette score for different k values and choose the one with the highest score.
Consider domain knowledge and the specific problem requirements when selecting k value.
Experiment with different k...
Precision and recall can be calculated using values from a confusion matrix.
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Where TP = True Positive, FP = False Positive, FN = False Negative
For an n x n confusion matrix, sum the values in each row and column to get TP, FP, and FN for each class
I applied via LinkedIn and was interviewed in Sep 2023. There were 2 interview rounds.
I have experience in porous media and using CFD software for simulation and analysis.
I have worked on projects involving flow through porous media such as soil, rocks, and filters.
Utilized CFD software like ANSYS Fluent and COMSOL Multiphysics for modeling and analyzing fluid flow in porous media.
Performed simulations to study heat transfer, mass transfer, and fluid flow behavior in porous materials.
Implemented boundar...
My M.Tech dissertation focuses on optimizing CO2-EOR processes, leveraging mechanical design principles and CAD/CAE tools.
My dissertation explores the optimization of CO2 injection rates, which directly impacts recovery efficiency in EOR.
I have experience using CAD software like SolidWorks to design components for CO2 injection systems, ensuring they meet operational requirements.
Through CAE tools such as ANSYS, I have...
I am a dedicated professional with a background in project management and a passion for driving impactful initiatives.
Educational Background: I hold a degree in Project Management from XYZ University, where I developed strong organizational skills.
Work Experience: I have over two years of experience as a project coordinator at ABC Company, successfully managing multiple projects.
Skills: Proficient in tools like MS Proj...
We were asked to talk about the research work done previously, and interests and what would u do here
General knowledge related to Indian history
I appeared for an interview before Jun 2024, where I was asked the following questions.
I align my past experiences with the skills and responsibilities required for the Assistant Project Engineer role.
I managed a construction project where I coordinated between teams, ensuring timely completion, similar to the role's requirements.
In my previous internship, I utilized project management software to track progress, which is essential for this position.
I have experience in budgeting and resource allocation ...
Yes, I am willing to travel as required for project needs and to ensure successful project execution.
Traveling allows me to oversee project sites and ensure quality control.
I have previously traveled for site inspections, which improved project outcomes.
I understand that travel may be necessary for client meetings and collaboration.
I am flexible with travel schedules and can adapt to project timelines.
I applied via Naukri.com and was interviewed before Dec 2023. There were 2 interview rounds.
WRITTEN TEST IN OBJECTIVE TYPE QUESTIONS
I am drawn to this organization for its commitment to excellence and the opportunity to contribute to a collaborative team environment.
I admire the company's values and mission, particularly its focus on community service and support.
The opportunity for professional growth and development aligns with my career goals, as I am eager to learn and advance.
I appreciate the positive work culture here, which fosters teamwork ...
I appeared for an interview before May 2024, where I was asked the following questions.
I applied via Walk-in and was interviewed before Apr 2023. There were 2 interview rounds.
I applied via Walk-in and was interviewed before Jun 2022. There were 3 interview rounds.
On the spot problem solving through design ideation
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The duration of IIT Guwahati interview process can vary, but typically it takes about less than 2 weeks to complete.
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Research Scholar
18
salaries
| ₹3.5 L/yr - ₹8 L/yr |
Teaching Assistant
17
salaries
| ₹1.5 L/yr - ₹4.9 L/yr |
Junior Research Fellow
15
salaries
| ₹3 L/yr - ₹5.2 L/yr |
Assistant Project Engineer
15
salaries
| ₹3.6 L/yr - ₹4.7 L/yr |
PHD Research Scholar
14
salaries
| ₹3.4 L/yr - ₹5 L/yr |
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