Natural Language Processing Engineer Interview Questions & Answers
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Prepare comprehensively for your Natural Language Processing Engineer interview with our extensive list of 78 questions. Each question is designed to test and expand your Natural Language Processing Engineer expertise. Suitable for all experience levels, these questions will help you prepare thoroughly. Access the free PDF to get all 78 questions and give yourself the best chance of acing your Natural Language Processing Engineer interview. This resource is perfect for thorough preparation and confidence building.

78 Natural Language Processing Engineer Questions and Answers:

Natural Language Processing Engineer Job Interview Questions Table of Contents:

Natural Language Processing Engineer Job Interview Questions and Answers
Natural Language Processing Engineer Job Interview Questions and Answers

1 :: Please explain what is Perceptron in Machine Learning?

In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of several possible non-binary outputs.

2 :: Explain me what is Genetic Programming?

Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

3 :: Tell me which method is frequently used to prevent overfitting?

When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue.

4 :: Explain what is ensemble learning?

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning.

5 :: Tell us what is dimension reduction in Machine Learning?

In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction

6 :: Tell me various approaches for machine learning?

The different approaches in Machine Learning are

☛ a) Concept Vs Classification Learning
☛ b) Symbolic Vs Statistical Learning
☛ c) Inductive Vs Analytical Learning

7 :: Tell us why ensemble learning is used?

Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.

8 :: Do you know what is the standard approach to supervised learning?

The standard approach to supervised learning is to split the set of example into the training set and the test.

9 :: Tell me what are the advantages of Naive Bayes?

In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data. The main advantage is that it can’t learn interactions between features.

10 :: Tell me when to use ensemble learning?

Ensemble learning is used when you build component classifiers that are more accurate and independent from each other.

11 :: Tell me what are Bayesian Networks (BN)?

Bayesian Network is used to represent the graphical model for probability relationship among a set of variables .

12 :: Do you know why overfitting happens?

The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.

13 :: Tell me what is classifier in machine learning?

A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

14 :: Tell me what are support vector machines?

Support vector machines are supervised learning algorithms used for classification and regression analysis.

15 :: Tell me what is Inductive Logic Programming in Machine Learning?

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.

16 :: Tell me what is not Machine Learning?

☛ a) Artificial Intelligence
☛ b) Rule based inference
☛ a) Decision Trees

☛ b) Neural Networks (back propagation)

☛ c) Probabilistic networks

☛ d) Nearest Neighbor

☛ e) Support vector machines

18 :: Tell me what are the two paradigms of ensemble methods?

The two paradigms of ensemble methods are

☛ a) Sequential ensemble methods
☛ b) Parallel ensemble methods

19 :: Tell us what are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are

☛ a) Supervised Learning
☛ b) Unsupervised Learning
☛ c) Semi-supervised Learning
☛ d) Reinforcement Learning
☛ e) Transduction
☛ f) Learning to Learn

20 :: Which of the following technique is not a part of flexible text matching?

A) Soundex
B) Metaphone
C) Edit Distance
D) Keyword Hashing

D) Keyword Hashing

Except Keyword Hashing all other are the techniques used in flexible string matching

22 :: Do you know what is Model Selection in Machine Learning?

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining.

23 :: Please explain what is PCA, KPCA and ICA used for?

PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

24 :: Tell me what is the difference between artificial learning and machine learning?

Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning. While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.