AuthBridge Research Services Learning Manager Interview Questions and Answers

Q1. Python question on list comprehension
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Q2. Python related questions
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Q1. About machine learning basics and on resume
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Q1. About resume and deep learning a bit off
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Q1. I didn't make up to HR round
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Q1. In depth questions on every ml algorithm from supervised to un supervised algorithms
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Q2. How to over come over fitting
Ans. 
To overcome overfitting, use techniques like cross-validation, regularization, early stopping, and increasing training data.
• Use cross-validation to evaluate model performance on different subsets of data.

• Apply regularization techniques like L1 or L2 regularization to penalize large coefficients.

• Implement early stopping to stop training when validation error starts to increase.

• Increase training data to provide more dive
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Q3. What is PCA, how to do feature selection
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PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
• PCA stands for Principal Component Analysis

• It works by finding the directions (principal components) in which the data varies the most

• These principal components are orthogonal to each other and capture the maximum variance in the data

• Feature selection can be done by selecting th...read more
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Q4. Project based questions
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Q1. Scenario based questions on implementing NLP use case
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It was 1 hour coding test with 2 questions. One was easy and another was medium level coding question.

Q1. Few SQL Queries
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Q2. Explain null hypothesis and p-value in terms of probability
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Null hypothesis is a statement that assumes no relationship or difference between variables. P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true.
• Null hypothesis is a statement that assumes no effect or relationship between variables

• P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true

• Null hy...read more
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Q3. Explain Linear and Logistic Regression
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Linear regression is used for predicting continuous numerical values, while logistic regression is used for predicting binary categorical values.
• Linear regression models the relationship between a dependent variable and one or more independent variables using a linear equation.

• Logistic regression models the probability of a binary outcome using a logistic function.

• Linear regression is used for tasks like predicting hou...read more
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Contain question related to aptitude, pyrhon,ml mcqs.

Q1. What is overfitting and underfitting?
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Overfitting occurs when a model learns the training data too well, leading to poor generalization. Underfitting happens when a model is too simple to capture the underlying patterns.
• Overfitting: Model performs well on training data but poorly on unseen data. Can be caused by a model being too complex or training for too long.

• Underfitting: Model is too simple to capture the underlying patterns in the data. Results in po...read more
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Q2. Explain about your project
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4 technical questions, 1 python code, 2 SQL, 1 Spark

It was an easy one with basic aptitude and basics of programming

Good level of coding questions

Q1. What is machine learning
Ans. 
Machine learning is a subset of artificial intelligence that involves the development of algorithms and statistical models to perform specific tasks without explicit instructions.
• Machine learning is a subset of artificial intelligence.

• It involves the development of algorithms and statistical models.

• These models are trained on data to make predictions or decisions.

• Examples include image recognition, natural language pro
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Q2. Types of machine learning and difference between ML and deep learning
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Types of machine learning include supervised, unsupervised, and reinforcement learning. Deep learning is a subset of ML using neural networks.
• Supervised learning: Uses labeled data to make predictions, such as classification or regression.

• Unsupervised learning: Finds patterns in unlabeled data, like clustering or dimensionality reduction.

• Reinforcement learning: Learns through trial and error to maximize rewards, common...read more
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Q1. Resume questions
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