Litmus7 Systems Consulting Ai Ml Engineer Interview Questions and Answers

Q1. Scenario based questions
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Q1. Steps of pre-processing
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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 ?
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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. 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. Difference between Lemmatization and stemming
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Lemmatization produces the base or dictionary form of a word, while stemming reduces words to their root form.
• Lemmatization considers the context and meaning of the word, resulting in a valid word that makes sense.

• Stemming simply chops off prefixes or suffixes, potentially resulting in non-existent words.

• Example: Lemmatization of 'better' would result in 'good', while stemming would reduce it to 'bet'.
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Q1. Q. explain your project Q. Project-related questions asked basic to hard Q. What is backpropagation in deep learning? Q. What is the Hugging Face module and working Q. Object detection YOLO NAS Vs YOLO 8
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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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