Genesys Ai Ml Engineer Interview Questions, Process, and Tips for Experienced

Q1. Question on NLP and Coding test
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Q2. Maximum coding question from list and Regex
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Q1. Project based question
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Q1. Explain the ML project you recently worked?
Ans. 
Developed a recommendation system for an e-commerce platform using collaborative filtering
• Used collaborative filtering to analyze user behavior and recommend products

• Implemented matrix factorization techniques to improve recommendation accuracy

• Evaluated model performance using metrics like RMSE and precision-recall curves
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Q2. Questions related to ML fundamentals like supervised learning, unsupervised learning, evaluation and ML algorithms
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Q3. Project specific questions
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Q4. Easy-medium coding questions
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Q1. What technologies you are working on?
Ans. 
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
• Python programming language

• TensorFlow framework

• scikit-learn library
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Q2. How you will approach on machine learning problem?
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I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
• Understand the problem statement and define the objectives clearly.

• Collect and preprocess the data to make it suitable for training.

• Select a suitable machine learning algorithm based on the problem type (clas...read more
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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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Discussion on Gradient, SGD, K Mean ++, Silhouette Score, How to Handle High Variation Data,
Coding asked to code KNN, Hyper-Parameter Tuning, Two Difficult Questions on Coding...DSA Based Stumped on Those.

Verdict... Not Selected

Simple Coding No Chat GPT Support Should Be There

Q1. Difference between Lemmatization and stemming
Ans. 
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. Normal HR round that happens as the initial screening process
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Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions

Q1. Normal salary discussion
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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. 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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