Crack Job Interview in Machine Learning using Python

Bhumika Gupta | Updated on 02 Mar, 2020 | AI Machine Learning Training Certification Courses Online | 1669

Machine Learning is taking 21st-century devices beyond unimaginable intelligence. The training of machines to be able to perform critical tasks with human interference and complex programming has made devices using virtual personal assistants, GPS navigation, video surveillance, and online fraud detection work efficiently. Python is one of the most widely used programming languages for Machine Learning. It is being used in industries such as gaming, automobile, aeronautics, education, and healthcare. The dynamic requirement demanded the skill makes it a strong opportunity for professional corporates who are interested in and related to Machine Learning.

Here is a collection of the 10 most commonly asked interview questions with their answers for a job in Machine Learning:

Question: Which library would you prefer for plotting in Python language: Seaborn or Matplotlib or Bokeh?

Answer: The answer to this question depends on which visualization you are trying to achieve. Every Library has a specific purpose:

Matplotlib: It is used for basic plotting like pies, bars, scatter, lines, plots, etc.

Seaborn: It is built to ease plotting and is used for statistical visualization. Examples of its use include showing the distribution of your data or heatmaps.

Question: How are NumPy and SciPy related?

Answer: NumPy is a part of SciPy and defines the basic numerical functions like indexes, reshaping, sorting etc. SciPy implements computations like Machine Learning using NumPy’s functionality.

Question: How is the decision tree pruned?

Answer: The branches of decision trees have branches having weak predictive power are removed for increasing predictive accuracy and reducing the complexity of the model. It can be pruned by continuing pruning and replacing each node unless the predictive accuracy of the required amount is obtained.

Question: When do we use L1 and L2 norm in Machine Learning?

Answer: When a regression model uses the L1 regularization technique, it is called Lasso regularization. On the other hand, when it uses L2, it is called Ridge Regression. Ridge regression is used to add “squared magnitude” to loss function of coefficient in the form of penalty term. Lamda Regression is used to add “absolute value of magnitude” to loss function of coefficient in the form of penalty term.

Question: How would you handle an imbalanced dataset?

Answer: An imbalances dataset can be handled in the following ways:

By collecting more data

By resampling it for correction of imbalances (undersampling/oversampling)

By trying a different algorithm on the dataset (bagging/ boosting classifiers)

By generating synthetic samples (S.M.O.T.E)

Question: List down some of the ways to visualize the data?

Answer: Data can be visualized using 2 kinds of plots:

By involving plots of one variety of quality for individual variables like histogram and Box plot.

By involving a different variety of plots such as Scatterplot matrix to understand the structuring of connection between interactions/relationship between the variables.

Question: What is Boosting and how does Boosting identify the weak learners?

Answer: The family of algorithms that can convert weak learners into a strong learner is called Boosting. The process is performed in a sequence by each model attempting to correct the errors of the previous model.

How to handle the Non-stationary in Time series data?

Answer: The mean of a time series can be stabilized by eliminating the changes in the level of a time series with the help of difference. This helps in removing trend and seasonality.

What does PCA do?

Answer: The dimensionality reduction algorithm PCA collects and decomposes data using transformations in Principal Components (PC). This statistical procedure converts a set of observations of correlated variables using statistical procedures for values that are not correlated variables, also known as principal components.

What is Multi-Dimensional scaling?

Multi-Dimensional scaling is an approach that uses mapping relating to the ability to interpret dimensions. Its purpose is transforming consumer judgments into distances that can be represented in multi-dimensional space. When the basis of comparison is unknown, MDS as an exploratory technique helps to examine unrecognized dimensions of the products and reveal the comparative evaluation of the products.

The knowledge of answering the interview question of Machine learning with Python can only be obtained after having skills related to the technology. You can learn Machine learning in Python anytime and anywhere with the help of Python Machine Learning online training course offered by Multisoft Virtual Academy. To read more about the course and to join it, click here. You may visit our website www.multisoftvirtualacademy.com to have a look at 600+ online courses we offer to professionals and corporate companies.

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About the Author

Bhumika Gupta

Writer, poet, and artist Bhumika Gupta has been awarded a National Certificate for her writings and is working as a Senior Content Writer at Multisoft. She is keenly interested in the training sector and has been passionately writing about corporate training courses for many years.

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Crack Job Interview in Machine Learning using Python

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