1⟩ 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
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2⟩ Tell me what is sequence learning?
Sequence learning is a method of teaching and learning in a logical manner.
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 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
5⟩ Please explain how can you avoid overfitting?
By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model.
In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.
6⟩ Tell me what are two techniques of Machine Learning?
The two techniques of Machine Learning are
☛ a) Genetic Programming
☛ b) Inductive Learning
7⟩ Tell us the function of ‘Supervised Learning’?
☛ a) Classifications
☛ b) Speech recognition
☛ c) Regression
☛ d) Predict time series
☛ e) Annotate strings
8⟩ Tell us what are the components of relational evaluation techniques?
The important components of relational evaluation techniques are
☛ a) Data Acquisition
☛ b) Ground Truth Acquisition
☛ c) Cross Validation Technique
☛ d) Query Type
☛ e) Scoring Metric
☛ f) Significance Test
9⟩ Collaborative Filtering and Content Based Models are the two popular recommendation engines, what role does NLP play in building such algorithms. A) Feature Extraction from text B) Measuring Feature Similarity C) Engineering Features for vector space learning model D) All of these
D) All of these
NLP can be used anywhere where text data is involved – feature extraction, measuring feature similarity, create vector features of the text.
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.
11⟩ 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
12⟩ Do you know ‘Overfitting’ in Machine learning?
In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.
13⟩ Social Media platforms are the most intuitive form of text data. You are given a corpus of complete social media data of tweets. How can you create a model that suggests the hashtags? A) Perform Topic Models to obtain most significant words of the corpus B) Train a Bag of Ngrams model to capture top n-grams – words and their combinations C) Train a word2vector model to learn repeating contexts in the sentences D) All of these
D) All of these
All of the techniques can be used to extract most significant terms of a corpus.
14⟩ 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.
15⟩ 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
16⟩ 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
17⟩ 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.
18⟩ What is the right order for a text classification model components Text cleaning Text annotation Gradient descent Model tuning Text to predictors A) 12345 B) 13425 C) 12534 D) 13452
C) 12534
A right text classification model contains – cleaning of text to remove noise, annotation to create more features, converting text-based features into predictors, learning a model using gradient descent and finally tuning a model.
19⟩ In Latent Dirichlet Allocation model for text classification purposes, what does alpha and beta hyperparameter represent- A) Alpha number of topics within documents, beta number of terms within topics False B) Alpha density of terms generated within topics, beta density of topics generated within terms False C) Alpha number of topics within documents, beta number of terms within topics False D) Alpha density of topics generated within documents, beta density of terms generated within topics True
D) Alpha: density of topics generated within documents, beta: density of terms generated within topics True
20⟩ Which of the following techniques can be used for the purpose of keyword normalization, the process of converting a keyword into its base form? Lemmatization Levenshtein Stemming Soundex A) 1 and 2 B) 2 and 4 C) 1 and 3 D) 1, 2 and 3 E) 2, 3 and 4 F) 1, 2, 3 and 4
C) 1 and 3
Lemmatization and stemming are the techniques of keyword normalization, while Levenshtein and Soundex are techniques of string matching.
