Standard Chartered – Machine Learning Interview Questions
Here is the list Machine Learning Interview Questions which are recently asked in Standard Chartered company. These questions are included for both Freshers and Experienced professionals.
1. List down various approaches for machine learning?
various approaches for machine learning are:- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
2. What is Inductive Logic Programming in Machine Learning?
Inductive logic programming is that the subfield of machine learning that uses first-order logic to represent hypotheses and data. Because first-order logic is expressive and declarative, inductive logic programming specifically targets problems involving structured data and background knowledge.
3. What is Model Selection in Machine Learning?
Model selection is that the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset. Model selection is the process of choosing one of the models as the final model that addresses the problem.
4. What is Perceptron in Machine Learning?
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
5. What are the different categories you can categorized the sequence learning process?
Sequence prediction, sequence generation, sequence recognition, and sequential decision making. These “problems” show how sequences are formulated. They show the patterns sequences follow and how these different sequence learning problems are associated with one other.
6. What is sequence learning?
Sequential learning is a type of learning during which one part of a task is learnt before the next. Serial organization is fundamental to human behaviour.
7. What are two techniques of Machine Learning ?
Two techniques of Machine Learning are: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
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8. What is the difference between Bias and Variance?
Bias is that the simplifying assumptions made by the model to make the target function easier to approximate. Variance is that the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.
9. What is the difference between supervised and unsupervised machine learning?
Supervised learning algorithms are trained using labeled data. Unsupervised learning algorithms are trained using unlabeled data. In supervised learning, input data is provided to the model along side with the output. In unsupervised learning, only input data is provided to the model.
10. How is KNN different from K-means clustering?
KNN represents a supervised classification algorithm which will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.
11. Comparision between Machine Learning and Big Data
Big Data is more of extraction and analysis of information from huge volumes of data. Machine Learning is more of using input data and algorithms for estimating unknown future results.Big data analysis is that the unique way of handling bigger and unstructured data sets using tools like Apache Hadoop, MongoDB.
12. Explain what is precision and Recall?
Precision means the percentage of your results which are relevant. On the other hand, recall refers to the percentage of total relevant results are correctly classified by your algorithm.
13. What is your favorite algorithm and also explain the algorithm in briefly in a minute?
An algorithm may be procedure or formula for solving a problem, based on conducting a sequence of specified actions. In mathematics and computer science, an algorithm usually means a small procedure that solves a recurrent problem.
14. What is the difference between Type 1 and Type 2 errors?
Type 1 error, in statistical hypothesis testing, is that the error caused by rejecting a null hypothesis when it is true. Type II error is the error that occurs when the null hypothesis is accepted when it is not true. Type I error is equivalent to false positive. Type II error is equivalent to a false negative.
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