Nettur Technical Training Foundation Interview Questions and Answers for Freshers

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?
Ans. 
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
Ans. 
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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