Natural Language Processing Engineer Interview Preparation Guide
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:
1 :: Tell me what is sequence learning?
Sequence learning is a method of teaching and learning in a logical manner.
2 :: Tell me what are the different methods for Sequential Supervised Learning?
The different methods to solve Sequential Supervised Learning problems are
☛ a) Sliding-window methods
☛ b) Recurrent sliding windows
☛ c) Hidden Markow models
☛ d) Maximum entropy Markow models
☛ e) Conditional random fields
☛ f) Graph transformer networks
☛ a) Sliding-window methods
☛ b) Recurrent sliding windows
☛ c) Hidden Markow models
☛ d) Maximum entropy Markow models
☛ e) Conditional random fields
☛ f) Graph transformer networks
3 :: Tell us what is bias-variance decomposition of classification error in ensemble method?
The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.
4 :: Tell me what are the two classification methods that SVM ( Support Vector Machine) can handle?
☛ a) Combining binary classifiers
☛ b) Modifying binary to incorporate multiclass learning
☛ b) Modifying binary to incorporate multiclass learning
5 :: Tell us what are the two methods used for the calibration in Supervised Learning?
The two methods used for predicting good probabilities in Supervised Learning are
☛ a) Platt Calibration
☛ b) Isotonic Regression
These methods are designed for binary classification, and it is not trivial.
☛ a) Platt Calibration
☛ b) Isotonic Regression
These methods are designed for binary classification, and it is not trivial.
6 :: Tell us in what areas Pattern Recognition is used?
Pattern Recognition can be used in
☛ a) Computer Vision
☛ b) Speech Recognition
☛ c) Data Mining
☛ d) Statistics
☛ e) Informal Retrieval
☛ f) Bio-Informatics
☛ a) Computer Vision
☛ b) Speech Recognition
☛ c) Data Mining
☛ d) Statistics
☛ e) Informal Retrieval
☛ f) Bio-Informatics
7 :: Explain me the function of ‘Unsupervised Learning’?
☛ a) Find clusters of the data
☛ b) Find low-dimensional representations of the data
☛ c) Find interesting directions in data
☛ d) Interesting coordinates and correlations
☛ e) Find novel observations/ database cleaning
☛ b) Find low-dimensional representations of the data
☛ c) Find interesting directions in data
☛ d) Interesting coordinates and correlations
☛ e) Find novel observations/ database cleaning
8 :: Tell me what are the three stages to build the hypotheses or model in machine learning?
☛ a) Model building
☛ b) Model testing
☛ c) Applying the model
☛ b) Model testing
☛ c) Applying the model
9 :: Tell us what is inductive machine learning?
The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.
10 :: Explain me the difference between Data Mining and Machine learning?
Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used.