Kuvempu University Interview Questions and Answers

Q1. How do you train semi supervised machine learning models?
Ans. 
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
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Q2. What are the parametric types of machine learning?
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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...read more
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Q3. What are the types of Machine Learning?
Ans. 
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...read more
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Q4. Explain Eigen value
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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...read more
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Q5. What is Eigen Vector
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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 ...read more
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Q6. What is the maths behind PCA
Ans. 
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 ...read more
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Q7. If you are given a small dataset of 300 samples, what would you choose over a neural network with more number of hidden layers or a neural network with one hidden layer. Justify your explanation in terms o...read more
Ans. 
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...read more
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Q8. Can a neural network accept complex number as input?
Ans. 
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...read more
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Q9. What is kmeans algorithm, Explain it?
Ans. 
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
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Q10. What are the evaluation metrics?
Ans. 
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...read more
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Q11. Machine learning vs Deep Learning
Ans. 
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 ...read more
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Q12. How do you select k value in kmeans algorithm?
Ans. 
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...read more
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Q13. How do you calculate precision & recall for n x n confusion matrix?
Ans. 
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
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Q1. Why do you want to join this industry?
Ans. 
I am passionate about designing innovative solutions and contributing to the advancement of technology in the mechanical engineering industry.
• Passion for designing and creating innovative solutions

• Interest in contributing to technological advancements

• Excitement for the challenges and opportunities in the industry
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Q2. What is the career growth potential in this field?
Ans. 
Career growth potential in the field of Mechanical Engineering is high due to continuous technological advancements and demand for skilled professionals.
• Opportunities for advancement into management roles such as project manager or engineering manager

• Specialization in areas such as robotics, aerospace, or renewable energy can lead to higher paying positions

• Further education such as obtaining a master's degree or PhD ca...read more
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Q1. About Experience
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Q2. About Academics and teaching
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Q3. Subjective handling
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Q1. General Strategies
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Q2. Leadership qualities
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Q3. Educational Background
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Q4. Student Handling
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Q5. Drop out Handling
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Q6. Marketing and Media
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Q7. Budgeting and practical
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Q1. What is the genome size of Chickpea?
Ans. 
The genome size of Chickpea is approximately 738 megabases.
• The genome size of Chickpea is estimated to be around 738 megabases.

• Chickpea (Cicer arietinum) is a diploid plant species with 2n=16 chromosomes.

• Genome sequencing of Chickpea has provided valuable insights into its genetic makeup and potential for crop improvement.
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Q2. What is Genomic Assisted Breeding?
Ans. 
Genomic Assisted Breeding is a breeding approach that uses genetic information to improve the efficiency and accuracy of breeding programs.
• Utilizes genetic markers to identify desirable traits in plants or animals

• Helps breeders select individuals with desired traits for breeding

• Accelerates the breeding process by predicting offspring performance based on genetic information

• Examples include using DNA markers to select f
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Q3. Tell us about Marker Assisted Breeding.
Ans. 
Marker Assisted Breeding is a technique used in plant breeding to select for desired traits based on genetic markers.
• Marker Assisted Breeding involves identifying genetic markers linked to specific traits of interest.

• These markers can be used to select plants with desired traits at an early stage of development.

• This technique helps in accelerating the breeding process by reducing the time and resources required for tra...read more
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Q4. Do you know about any data analysis software?
Ans. 
Yes, I am familiar with data analysis software such as Microsoft Excel, SPSS, and Tableau.
• Microsoft Excel is commonly used for data analysis and visualization

• SPSS is a statistical software used for analyzing data

• Tableau is a data visualization tool that helps in creating interactive dashboards
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Q5. Do you have any skills in hydroponics?
Ans. 
Yes, I have experience in hydroponics including setting up systems, monitoring plant growth, and adjusting nutrient levels.
• Experience in setting up hydroponic systems

• Monitoring plant growth in hydroponic environments

• Adjusting nutrient levels for optimal plant growth

• Knowledge of different types of hydroponic systems such as NFT, DWC, and drip systems
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Q6. Explain field practices of Chickpea.
Ans. 
Chickpea field practices involve proper soil preparation, planting, irrigation, pest control, and harvesting.
• Soil preparation includes plowing, harrowing, and adding organic matter.

• Planting is typically done in rows with proper spacing between seeds.

• Irrigation is crucial, especially during flowering and pod development stages.

• Pest control may involve using natural predators or pesticides as needed.

• Harvesting is done wh...read more
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Q7. Tell us about Morphology and anatomy of Chickpea plant
Ans. 
Chickpea plant has a distinct morphology and anatomy, with compound leaves and distinctive flowers.
• Chickpea plants have compound leaves with 3 leaflets.

• The plant produces white or pink flowers with blue, violet, or pink veins.

• Chickpea pods contain 1-2 seeds, which are the edible part of the plant.

• The roots of the chickpea plant have nodules that contain nitrogen-fixing bacteria.
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Q1. Experience skills
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Q2. Over all experience
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Q1. What will you do if the patient's BP is low during dialysis
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Q2. Emergency drugs use during dialysis
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Q3. How many type of access use for dialysis
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Q1. What is important of RO in dialysis
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Q2. How many alarms are there in a dialysis machine
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Q1. Genral questions related to future
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Q1. Subject related queries
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