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
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
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.
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
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
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
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.
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.
12. Tell me what are two techniques of Machine Learning?
The two techniques of Machine Learning are
☛ a) Genetic Programming
☛ b) Inductive Learning
13. 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
14. Tell us the function of ‘Supervised Learning'?
☛ a) Classifications
☛ b) Speech recognition
☛ c) Regression
☛ d) Predict time series
☛ e) Annotate strings
15. 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.
C) 1 and 3
Lemmatization and stemming are the techniques of keyword normalization, while Levenshtein and Soundex are techniques of string matching.
D) Alpha: density of topics generated within documents, beta: density of terms generated within topics True
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.
D) All of these
All of the techniques can be used to extract most significant terms of a corpus.
20. 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.
The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.
22. Tell me what is batch statistical learning?
Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.
23. Tell me what are the different categories you can categorized the sequence learning process?
☛ a) Sequence prediction
☛ b) Sequence generation
☛ c) Sequence recognition
☛ d) Sequential decision
C) 9
Bigrams: Analytics Vidhya, Vidhya is, is a, a great, great source, source to, To learn, learn data, data science
D) 1 and 2
Choices A and B are correct because stopword removal will decrease the number of features in the matrix, normalization of words will also reduce redundant features, and, converting all words to lowercase will also decrease the dimensionality.
