Thirumoolar Software Machine Learning Engineer Intern Interview Questions and Answers for Freshers

Q1. ML algorithms, Live screen share coding
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Q1. ML deployment, Cloud knowledge
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Q1. Basics of DL and NLP , along with current projects
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Q1. General scenario based on leadership and decision making
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Q1. Implementation of end to end to projects What are transformers
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Transformers are models that process sequential data by learning contextual relationships between words.
• Transformers are a type of deep learning model commonly used in natural language processing tasks.

• They are based on the attention mechanism, allowing them to focus on different parts of the input sequence.

• Examples of transformer models include BERT, GPT, and TransformerXL.
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Q1. Machine Learning Fundamentals
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Q2. Python Advanced
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Q1. Why this company
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Q1. What is evaluation Matrix for classification
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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. Explain bias variance
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Bias variance tradeoff is a key concept in machine learning that deals with the balance between underfitting and overfitting.
• Bias refers to the error that is introduced by approximating a real-life problem, while variance refers to the amount by which the estimate of the target function will change if different training data was used.

• High bias means the model is too simple and underfits the data, while high variance me...read more
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Q2. Explain ensemble learni
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Ensemble learning is a technique of combining multiple machine learning models to improve the overall performance.
• Ensemble learning can be done in two ways: bagging and boosting.

• Bagging involves training multiple models independently on different subsets of the data and then combining their predictions.

• Boosting involves training models sequentially, with each model trying to correct the errors of the previous model.

• Ens...read more
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Q1. What is Salary expectations
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