KS Smart Solutions Machine Learning Engineer Interview Questions and Answers

Q1. What is evaluation Matrix for classification
Ans. 
Evaluation metrics for classification are used to assess the performance of a classification model.
• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the proportion of correctly classified instances out of the total instances.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive p...read more
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Q2. What L1 and L2 regression
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L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.
• L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.

• L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.

• L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.

• L1 regularization is also know...read more
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Q3. Explain random forest algorithm
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Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.
• Random forest creates multiple decision trees using bootstrapping and feature randomization.

• Each tree in the random forest is trained on a subset of the data and features.

• The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).
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Q1. Tell me about self
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I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.
• Experienced in developing machine learning models for various applications

• Proficient in programming languages such as Python, R, and Java

• Skilled in data preprocessing, feature engineering, and model evaluation

• Strong understanding of algorithms and statistical concepts

• Excellent problem-solving and ana
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Q2. Questions about salary discuss
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Q1. Basic Stats questions
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Q2. Basic ML questions
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Q1. Started with basic os or network fundamentals like analyzing a core dump, handling tcp packet loss, difference between rest api and grpc etc. Then, from ML started with basic questions like bias variance t...read more
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Q1. Difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is error introduced by approximating a real-world problem, leading to underfitting.

• Variance is error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to be overl...read more
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Q2. What’s is Learning rate
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Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.
• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.

• A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.

• Com...read more
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Problem solving skills

Logical reasoning numerical reasoning abstract reasoning verbal reasoning

Q1. Self introduction
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Q2. Family background
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Q3. Salary expectation
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Q1. Explain the latest project you worked on in terms of your role in it.
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Q1. Behavioral and situational questions
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Q1. ML algorithms, Live screen share coding
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Q1. ML deployment, Cloud knowledge
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Machine learning - Code K-Means

Machine Learning - Code Neural Network

Q1. Machine Learning Fundamentals
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Q2. Python Advanced
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Q1. Why this company
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