FullThrottle Labs Machine Learning Intern Interview Questions and Answers

Q1. Basic NLP topics like stemming, tokenizing, lemmatization etc.
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Q2. Difference between BOW and Count Vectorizer
Ans. 
BOW and Count Vectorizer are both techniques used for text representation in NLP.
• BOW stands for Bag of Words and represents text as a collection of words without considering the order.

• Count Vectorizer is a technique that counts the frequency of each word in a document and represents it as a vector.

• BOW is a simpler technique and is used for tasks like sentiment analysis, while Count Vectorizer is used for more complex t...read more
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Q3. What goes on during an NER process
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NER process identifies and extracts named entities from text data.
• NER stands for Named Entity Recognition.

• It involves identifying and classifying entities such as people, organizations, locations, and dates.

• NER can be performed using rule-based systems or machine learning algorithms.

• Examples of NER applications include information extraction, sentiment analysis, and chatbots.

• Popular NER tools include spaCy, NLTK, and S
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Q4. Different types of NER libraries and their performances
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There are various NER libraries available with different performances.
• Stanford NER - high accuracy but slow processing

• SpaCy - fast and accurate, supports multiple languages

• NLTK - widely used, but lower accuracy compared to others

• Flair - contextual embeddings for better accuracy

• BERT - pre-trained models for NER tasks

• CRF++ - Conditional Random Fields for NER

• GATE - rule-based and machine learning-based NER

• OpenNLP - Java-b
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Q5. Spacy Pipelines.
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Q6. Basic ML questions in regards to modelling, clustering were asked as well.
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Was a half hour session. Was asked to dry run a code given, then basic leetcode esque questions like reverse a number, anagrams etc.

Assignment was to perform NER based on a resume given training data