Chapter 8 – 100 NLP Interview Questions
NLP Interview Questions
NLP Interview Questions
Topic – 50 Most Asked Logistic Regression Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will definitely depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned in order to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
2200 Analytics Interview Questions
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Intervie
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with Solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution
Chapter 5 – Case Study and Guesstimate – 100 Case Study and Guesstimate with a complete solution
Chapter 6 -Linear Regression – 50 Most Asked Linear Regression Interview Questions with solution
Chapter 7 – Logistic Regression – 50 Most Asked Logistic Regression Interview Questions with solution
Chapter 8 – Natural Language Processing – 100 Most Asked NLP Questions with Solution
855. What is NLP?
856. What are the uses of NLP?
857. What are the different algorithms in NLP?
858. What problems can NLP solve?
859. What is Regular Expression?
860. What are the packages in Python to help in Regular Expression
861.What is the difference between match and search function?
862. Guess the output of the following
import re
re.split(‘\s’,’The Data Monk is cool’)
863.Work in finding the output of the following
regx = r”\w+”
strx = “This isn’t my pen”
re.findall(regx,strx)
864. How to write a regular expression to match some specific set of characters in a string?
865. Write a regular expression to split a paragraph every time it finds an exclamation mark
867. Find the output of the following code?
868. What is tokenization?
869. What is NLTK?
870. What are the important nltk tokenizer?
871. What is the use of the function set() ?
872. Now get the unique words from the above paragraph
873. What is the use of .start() and .end() function?
874. What is the OR method?
875. What are the advance tokenization techniques?
876. How to write a regex to match spaces or commas?
877. How to include special characters in a regex?
878. What is the difference between (a-z) and [A-Z]?
879. Once again go through the difference between search() and match() function.
880. What is topic modeling?
881. What is bag-of-words?
882. How to counter the case sensitive nature of bag-of-words?
883. What is counter?
884. How to import Counter in Python?
885. Use the same paragraph used above and print the top 3 most common words
886. What is text preprocessing?
887. What are the commonly used methods of text preprocessing?
888. How to tokenize only words from a paragraph while ignoring the numbers and other special
Character?
889. What are stop words?
890. How to remove stop words from my text?
891. What is Lemmatization?
892. Give an example of Lemmatization in Python
893. How to lemmatize the texts in your paragraph?
894. What is gensim?
895. What is a word vector?
896. What is LDA?
897. What is gensim corpus?
898. What is stemming?
899. Give an example of stemming in Python
900. What is tf-idf?
901. How to create a tf-idf model using gensim?
902. What is Named Entity Recognition?
903. What is POS?
904. What is the difference between lemmatization and stemming?
905. What is spacy package?
906. How to initiate the English module in spacy?
907. Why should one prefer spacy over nltk for named entity recognition?
908. What are the different packages which uses word vectors?
909. What if your text is in various different languages? Which package can help you in Named
Entity Recognition for most of the largely spoken languages?
910. What is supervised learning?
911. How can you use Supervised Learning in NLP?
912. What is Naïve-Bayes model?
913. What is the flow of creating a Naïve Bayes model?
914. What is TF-IDF?
915. What is POS?
916. Take an example to take a sentence and break it into tokens i.e. each word
917.. Take the same sentence and get the POS tags
918. Take the following line and break it into tokens and tag POS using function
919. What is NER?
920. What are some of the common tags in POS.
921. Implement NER on the tokenized and POS tagged sentence used above.
922. What are n-grams?
923. Create a 3-gram of the sentence below
“The Data Monk was started in Bangalore in 2018″
924. What is the right order for a text classification model components?
925. What is CountVectorizer?
926. How to create a dataset? What to write in it?
927. What all packages do I need to import for this project?
928. How to import a csv file in Python?
929. Let’s view the top and bottom 5 lines of the file to make sure we are good to go with the
Analysis
930. Now we will clean the dataset. Will start with removing numbers and punctuations. Write
a regular expression for removing special characters and numbers review is the name of the data set and Review is the name of the column
931. Now we want to stem the words. Do you remember the definition of stemming?
932. What does the above snippet do?
933. Create the final dataset with only stemmed words.
934. How to use the CountVectorizer() function? Explain using an example
935. Now let’s apply CountVectorizer on our dataset
936. How to separate the dependent variable?
937. Now we need to split the complete data set into train and test
938. Random forest is one of the best model to work on supervised learning. By the way, what
is Random forest?
939. What is Random Forest?
940. Let’s create our Random forest model here
941. Define n_estimator
942. Define criterion. Why did you use entropy and not gini?
943. What is model.fit()?
944. Let’s predict the output for the testing dataset
945. Now let’s check the confusion matrix to see how many of our outputs were correct
946. Lastly, what is confusion matrix and how to know the accuracy of the model?
947. What is sub() method?
948.Convert all the text into lower case and split the words
The Data Monk services
We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now
- YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
Link – The Data Monk Youtube Channel - Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
Link – The Data Monk website - E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
Link – The Data E-shop Page - Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
Link – The Data Monk Instagram page - Mock Interviews/Career Guidance/Mentorship/Resume Making
Book a slot on Top Mate
The Data Monk e-books
We know that each domain requires a different type of preparation, so we have divided our books in the same way:
1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics
You can check out all the other e-books on our e-shop page – Do not miss it
For any information related to courses or e-books, please send an email to [email protected]
