Quantiphi Analytics Solutions Private Limited Data Scientist and Machine Learning Engineer Interview Questions, Process, and Tips

Q1. Tell me about yourself and the projects you have worked on so far?
Ans. 
I am a data scientist and machine learning engineer with experience in developing predictive models for various industries.
• Developed a predictive maintenance model for a manufacturing company to reduce downtime and maintenance costs.

• Built a recommendation system for an e-commerce platform to personalize product recommendations for users.

• Worked on a natural language processing project to classify customer reviews for se...read more
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Q1. Different scores for model evaluations, embedding models
Ans. 
Different scores like accuracy, precision, recall, F1 for evaluating embedding models
• Common evaluation metrics for embedding models include accuracy, precision, recall, and F1 score

• Accuracy measures overall correctness of the model's predictions

• Precision measures the proportion of true positive predictions among all positive predictions

• Recall measures the proportion of true positive predictions among all actual positiv...read more
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Q2. How embedding models work
Ans. 
Embedding models learn to represent words or entities as dense vectors in a continuous vector space.
• Embedding models map words or entities to high-dimensional vectors where similar words have similar vectors.

• These models are trained using neural networks to learn the relationships between words based on their context.

• Popular embedding models include Word2Vec, GloVe, and FastText.

• Embedding models are commonly used in na...read more
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Q3. What is difference between precision and recall
Ans. 
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.
• Precision focuses on the accuracy of positive predictions, while recall focuses on the proportion of actual positives that were correctly identified.

• Precision = TP / (TP + FP), Recall = TP / (TP + ...read more
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Q1. How word2vec works, how gensim works. what is tf-idf
Ans. 
word2vec is a technique to create word embeddings, gensim is a Python library for topic modeling and similarity detection, tf-idf is a method to represent the importance of a word in a document.
• word2vec is a neural network model that learns word embeddings by predicting the context of a word based on its surrounding words.

• Gensim is a Python library for topic modeling, document similarity analysis, and other natural lan...read more
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Q1. Gcp/aws expertise, traditional ml, mlops
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Q2. K8, mlops, deep learning
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Q1. Q&A on ML and Deep Learning concepts
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Q1. Exp related to pure Deep Learning questions
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Q1. Why was I leaving my previous company and general HR questions
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