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Data Science Master Course

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Date: Mon, 20 Mar 2023

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Try answering these questions before facing Data Science interviews:


1. What is the difference between Supervised Learning and Unsupervised Learning?
Machine Learning is a type of machine learning on which machines are trained using ‘labeled’ dataset to derive or predict the output. The labeled data here means that some input is already tagged with the right output. For eg., take an example of an automatic car. We have to provide it with a route map, and with the help of sensors it can negotiate traffic and vehicles on the roads and automatically stop when there is a traffic junction, and finally reach the destination.
Unsupervised Machine Learning uses machine learning algorithms to analyze and cluster unlabeled datasets and make predictions. For eg., for identifying diabetics in a person early, a large dataset of previous past history medical data is provided to the machine and the machine makes a learning with the algorithms at its command, compares with the normal diabetic reading of patients, and compares the reading of patient, and finally makes the prediction. Another example could be to predict the customer’s future buying options based on the past buying behavior.


2. What is the difference between machine learning and deep learning?
Machine Learning (ML) is a subset of Artificial Intelligence (AI), that focuses on development of algorithm and statistical models, to enable the system to learn and identify patterns and relationships from a dataset and make predictions, or analyze the trends and make projections for the future.
Deep Learning (DL) is the subset of Machine Learning (ML) that uses neural networks to analyze complex patterns and relationship in the dataset. In other words, it will take a complex dataset and the data is moved through the various layers in the neural network until we get a final segregated data of a particular type. It resembles a human brain as to how the neurons are interconnected to each other. Deep Learning models can be trained using large amount of data and algorithm , and they have the ability to learn and improve overtime with huge datasets.


3. What are the problems related to Overfitting and Underfitting and how will you deal with these ?
Both Overfitting and Underfitting causes degraded performance of Machine Learning models.
Overfitting occurs when we train our model more than required ie., the more we train our model, the more the chances of overfitting. Such problem occurs on supervised models.
Underfitting occurs when the model is not able to learn with the training data provided. So provide more training data to sort the issue.
The goal of machine learning should be “Goodness to Fit” means ie the result of predicted value should match the true value of data set.
Three important methods to avoid overfitting are:
• Keeping the model simple—using fewer variables and removing major amount of the noise in the training data
• Using cross-validation techniques. E.g.: k folds cross-validation
• Using regularisation techniques — like LASSO, to penalise model parameters that are more likely to cause overfitting.


4. What is the importance of Data Cleansing?
Data Cleansing is a process of removing or updating the information that is incorrect, incomplete, duplicated, irrelevant, or formatted improperly. It is very important to improve the quality of data and hence the accuracy and productivity of the processes.


5 What libraries do data scientists use to plot data in Python?
The libraries used for data plotting are:
seaborn
ggplot
matplotlib


6. Explain Eigenvectors and Eigenvalues
Eigenvectors depict the direction in which a linear transformation moves and acts by compressing, flipping, or stretching. They are generally used to calculate the correlation or covariance matrix. The direction remains constant when a linear transformation is applied.
The Eigenvalues represent the strength of the transformation in the direction of Eigenvector.


7. What are Autoencoders?
They are artificial neural networks that tries to generate a representation as close as possible to the original input by training the network to ignore signal “noise” in between. It is used in unsupervised Machine Learning.


8. Differentiate between univariate, bivariate, and multivariate analysis.
Univariate data, contains only one variable. The univariate analysis describes the data and finds patterns that exist within it.
Bivariate data contains two different variables. The bivariate analysis deals with causes, relationships and analysis between those two variables.
Multivariate data contains three or more variables. Multivariate analysis is similar to that of a bivariate, however, in a multivariate analysis, there exists more than one dependent variable.


9) What is pruning in Decision Tree?
Pruning is the process of reducing the size of a decision tree. The reason for pruning is that the trees prepared by the base algorithm can be prone to overfitting as they become incredibly large and complex.


10. What are the various classification algorithms?
Different types of classification algorithms include logistic regression, SVM, Naive Bayes, decision trees, and random forest.


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I hadn't heard the term ‘cisgender’ until my argument with SAS about their teachings and my immediate impression was that it had a negative connotation… after looking it up, it is apparently a term invented in 1994 by the trans community to label anyone who is non-trans. The liberals then ate it up and regurgitated it to our kids, along with the pronouns and alphabet soup.

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Re: Explicit books in international school libraries

❰❰ Quote:

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