Vensure HCM Ai Ml Engineer Interview Questions and Answers

Q1. What is OHE ( one hot encoding)
Ans. 
OHE is a technique used in machine learning to convert categorical data into a binary format.
• OHE is used to convert categorical variables into a format that can be provided to ML algorithms.

• Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).

• For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0],
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Q2. What is conditional probability
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Conditional probability is the likelihood of an event occurring given that another event has already occurred.
• Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)

• It represents the probability of event A happening, given that event B has already occurred

• Conditional probability is used in various fields such as machine learning, statistics, and finance
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Q3. What is precision, recall
Ans. 
Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision is important when the cost of false positives is high, while recall is i...read more
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Q1. Scenario based questions
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Q1. What is zip function
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The zip function in Python is used to combine multiple iterables into a single iterable of tuples.
• Zip function takes two or more iterables as arguments and returns an iterator of tuples where the i-th tuple contains the i-th element from each of the input iterables.

• If the input iterables are of different lengths, the resulting iterator will only have as many elements as the shortest input iterable.

• Example: zip([1, 2, 3...read more
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Q2. What is NLP in Machine learning
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NLP (Natural Language Processing) in machine learning is the ability of a computer to understand, interpret, and generate human language.
• NLP enables machines to analyze and derive meaning from human language data.

• It involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.

• Examples of NLP applications include chatbots, language translation services, and speech rec
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Q1. About Handling Missing Values
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Q2. Questions on Embedding
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Q1. Question on random forest and linear regression
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Okay asked questions about ML , python , NLP , LLM

Q1. Experience or relevancy
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Q2. Communicate testing
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Q1. Steps of pre-processing
Ans. 
Pre-processing steps involve cleaning, transforming, and preparing data for machine learning models.
• Data cleaning: removing missing values, duplicates, and outliers

• Data transformation: scaling, encoding categorical variables, and feature engineering

• Data normalization: ensuring all features have the same scale

• Data splitting: dividing data into training and testing sets
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Q2. What is lemmatization ?
Ans. 
Lemmatization is the process of reducing words to their base or root form.
• Lemmatization helps in standardizing words for analysis.

• It reduces inflected words to their base form.

• For example, 'running' becomes 'run' after lemmatization.
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Q1. Confusion matrix
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