Which type of Machine learning Data Scientist generally used for solving classification and regression
problems?
A
Explanation:
Supervised Learning
Overview:
Supervised learning is a type of machine learning that uses labeled data to train machine learning
models. In labeled data, the output is already known. The model just needs to map the inputs to the
respective outputs.
Algorithms:
Some of the most popularly used supervised learning algorithms are:
· Linear Regression
· Logistic Regression
· Support Vector Machine
· K Nearest Neighbor
· Decision Tree
· Random Forest
· Naive Bayes
Working:
Supervised learning algorithms take labelled inputs and map them to the known outputs, which
means you already know the target variable.
Supervised Learning methods need external supervision to train machine learning models. Hence,
the name supervised. They need guidance and additional information to return the desired result.
Applications:
Supervised learning algorithms are generally used for solving classification and regression problems.
Few of the top supervised learning applications are weather prediction, sales forecasting, stock price
analysis.
Which of the learning methodology applies conditional probability of all the variables with respec-
tive the dependent variable?
A
Explanation:
Supervised learning methodology applies conditional probability of all the variables with respective
the dependent variable and generally conditional probability of variables is nothing but a basic
method of estimating the statistics for few random experiments.
Conditional probability is thus the likelihood of an event or outcome occurring based on the
occurrence of some other event or prior outcome. Two events are said to be independent if one
event occurring does not affect the probability that the other event will occur.
In a simple linear regression model (One independent variable), If we change the input variable by 1
unit. How much output variable will change?
D
Explanation:
What is linear regression?
Linear regression analysis is used to predict the value of a variable based on the value of another
variable. The variable you want to predict is called the dependent variable. The variable you are
using to predict the other variable's value is called the independent variable.
Linear regression attempts to model the relationship between two variables by fitting a linear
equation to observed data. One variable is considered to be an explanatory variable, and the other is
considered to be a dependent variable. For example, a modeler might want to relate the weights of
individuals to their heights using a linear regression model.
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable
and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when
x = 0).
For linear regression Y=a+bx+error.
If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y
increases by its slope.
For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1)
which implies Y=a+bx+b. So Y increases by its slope.
There are a couple of different types of classification tasks in machine learning, Choose the Correct
Classification which best categorized the below Application Tasks in Machine learning?
· To detect whether email is spam or not
· To determine whether or not a patient has a certain disease in medicine.
· To determine whether or not quality specifications were met when it comes to QA (Quality
Assurance).
C
Explanation:
The Supervised Machine Learning algorithm can be broadly classified into Regression and
Classification Algorithms. In Regression algorithms, we have predicted the output for continuous
values, but to predict the categorical values, we need Classification algorithms.
What is the Classification Algorithm?
The Classification algorithm is a Supervised Learning technique that is used to identify the category
of new observations on the basis of training data. In Classification, a program learns from the given
dataset or observations and then classifies new observation into a number of classes or groups. Such
as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or
categories.
Unlike regression, the output variable of Classification is a category, not a value, such as "Green or
Blue", "fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique,
hence it takes labeled input data, which means it contains input with the corresponding output.
In classification algorithm, a discrete output function(y) is mapped to input variable(x).
y=f(x), where y = categorical output
The best example of an ML classification algorithm is Email Spam Detector.
The main goal of the Classification algorithm is to identify the category of a given dataset, and these
algorithms are mainly used to predict the output for the categorical data.
The algorithm which implements the classification on a dataset is known as a classifier. There are two
types of Classifications:
Binary Classifier: If the classification problem has only two possible outcomes, then it is called as
Binary Classifier.
Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc.
Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as
Multi-class Classifier.
Example: Classifications of types of crops, Classification of types of music.
Binary classification in deep learning refers to the type of classification where we have two class
labels – one normal and one abnormal. Some examples of binary classification use:
· To detect whether email is spam or not
· To determine whether or not a patient has a certain disease in medicine.
· To determine whether or not quality specifications were met when it comes to QA (Quality
Assurance).
For example, the normal class label would be that a patient has the disease, and the abnormal class
label would be that they do not, or vice-versa.
As is with every other type of classification, it is only as good as the binary classification dataset that
it has – or, in other words, the more training and data it has, the better it is.
Which of the following method is used for multiclass classification?
A
Explanation:
Binary vs. Multi-Class Classification
Classification problems are common in machine learning. In most cases, developers prefer using a
supervised machine-learning approach to predict class tables for a given dataset. Unlike regression,
classification involves designing the classifier model and training it to input and categorize the test
dataset. For that, you can divide the dataset into either binary or multi-class modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This
makes it easy to filter the data, apply classification algorithms, and train the model to predict
outcomes. On the other hand, multi-class classification is applicable when there are more than two
class labels in the input train data. The technique enables developers to categorize the test data into
multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multi-
class approach depends on the classification technique. Below are the two models of the multi-class
classification algorithm.
One-Vs-Rest Classification Model for Multi-Class Classification
Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary
classification algorithm for multi-class classifications. The technique involves splitting a multi-class
dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle
each binary classification model with the most confident one making predictions.
For instance, with a multi-class classification problem with red, green, and blue datasets, binary
classification can be categorized as follows:
Problem one: red vs. green/blue
Problem two: blue vs. green/red
Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three
classes require three models from the above datasets, which can be challenging for large sets of data
with million rows, slow models, such as neural networks and datasets with a significant number of
classes.
The one-vs-rest approach requires individual models to prognosticate the probability-like score. The
class index with the largest score is then used to predict a class. As such, it is commonly used for
classification algorithms that can naturally predict scores or numerical class membership such as
perceptron and logistic regression.
Which ones are the key actions in the data collection phase of Machine learning included?
A, B
Explanation:
The key actions in the data collection phase include:
Label: Labeled data is the raw data that was processed by adding one or more meaningful tags so
that a model can learn from it. It will take some work to label it if such information is missing
(manually or automatically).
Ingest and Aggregate: Incorporating and combining data from many data sources is part of data
collection in AI.
Data collection
Collecting data for training the ML model is the basic step in the machine learning pipeline. The
predictions made by ML systems can only be as good as the data on which they have been trained.
Following are some of the problems that can arise in data collection:
Inaccurate data. The collected data could be unrelated to the problem statement.
Missing data. Sub-data could be missing. That could take the form of empty values in columns or
missing images for some class of prediction.
Data imbalance. Some classes or categories in the data may have a disproportionately high or low
number of corresponding samples. As a result, they risk being under-represented in the model.
Data bias. Depending on how the data, subjects and labels themselves are chosen, the model could
propagate inherent biases on gender, politics, age or region, for example. Data bias is difficult to
detect and remove.
Several techniques can be applied to address those problems:
Pre-cleaned, freely available datasets. If the problem statement (for example, image classification,
object recognition) aligns with a clean, pre-existing, properly formulated dataset, then take ad-
vantage of existing, open-source expertise.
Web crawling and scraping. Automated tools, bots and headless browsers can crawl and scrape
websites for data.
Private data. ML engineers can create their own data. This is helpful when the amount of data
required to train the model is small and the problem statement is too specific to generalize over an
open-source dataset.
Custom data. Agencies can create or crowdsource the data for a fee.
Which ones are the type of visualization used for Data exploration in Data Science?
A, D, E
Explanation:
Type of visualization used for exploration:
· Correlation heatmap
· Class distributions by feature
· Two-Dimensional density plots.
All the visualizations are interactive, as is standard for Plotly.
For More details, please refer the below link:
https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41
Which one is not the feature engineering techniques used in ML data science world?
D
Explanation:
Feature engineering is the pre-processing step of machine learning, which is used to transform raw
data into features that can be used for creating a predictive model using Machine learning or
statistical Modelling.
What is a feature?
Generally, all machine learning algorithms take input data to generate the output. The input data re-
mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-
tributes), and these attributes are often known as features. For example, an image is an instance in
computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an
observation, and the word count could be the feature. So, we can say a feature is an attribute that
impacts a problem or is useful for the problem.
What is Feature Engineering?
Feature engineering is the pre-processing step of machine learning, which extracts features from raw
data. It helps to represent an underlying problem to predictive models in a better way, which as a
result, improve the accuracy of the model for unseen data. The predictive model contains predictor
variables and an outcome variable, and while the feature engineering process selects the most useful
predictor variables for the model.
Some of the popular feature engineering techniques include:
1. Imputation
Feature engineering deals with inappropriate data, missing values, human interruption, general
errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance
of the algorithm, and to deal with them "Imputation" technique is used. Imputation is responsible for
handling irregularities within the dataset.
For example, removing the missing values from the complete row or complete column by a huge
percentage of missing values. But at the same time, to maintain the data size, it is required to impute
the missing data, which can be done as:
For numerical data imputation, a default value can be imputed in a column, and missing values can
be filled with means or medians of the columns.
For categorical data imputation, missing values can be interchanged with the maximum occurred
value in a column.
2. Handling Outliers
Outliers are the deviated values or data points that are observed too away from other data points in
such a way that they badly affect the performance of the model. Outliers can be handled with this
feature engineering technique. This technique first identifies the outliers and then remove them out.
Standard deviation can be used to identify the outliers. For example, each value within a space has a
definite to an average distance, but if a value is greater distant than a certain value, it can be
considered as an outlier. Z-score can also be used to detect outliers.
3. Log transform
Logarithm transformation or log transform is one of the commonly used mathematical techniques in
machine learning. Log transform helps in handling the skewed data, and it makes the distribution
more approximate to normal after transformation. It also reduces the effects of outliers on the data,
as because of the normalization of magnitude differences, a model becomes much robust.
4. Binning
In machine learning, overfitting is one of the main issues that degrade the performance of the model
and which occurs due to a greater number of parameters and noisy data. However, one of the
popular techniques of feature engineering, "binning", can be used to normalize the noisy data. This
process involves segmenting different features into bins.
5. Feature Split
As the name suggests, feature split is the process of splitting features intimately into two or more
parts and performing to make new features. This technique helps the algorithms to better
understand and learn the patterns in the dataset.
The feature splitting process enables the new features to be clustered and binned, which results in
extracting useful information and improving the performance of the data models.
6. One hot encoding
One hot encoding is the popular encoding technique in machine learning. It is a technique that
converts the categorical data in a form so that they can be easily understood by machine learning
algorithms and hence can make a good prediction. It enables group the of categorical data without
losing any information.
Skewness of Normal distribution is ___________
C
Explanation:
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical
explanation for mathematical proofs, you can refer to books or websites that speak on the same in
detail.
What is the formula for measuring skewness in a dataset?
C
Explanation:
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-
nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
A, B, C, D
Explanation:
All are correct!
About the Snowflake Marketplace
You can use the Snowflake Marketplace to discover and access third-party data and services, as well
as market your own data products across the Snowflake Data Cloud.
As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings
with many consumers simultaneously, rather than maintain sharing relationships with each indi-
vidual consumer. With Paid Listings, you can also charge for your data products.
As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-
cess the following:
Historical data for research, forecasting, and machine learning.
Up-to-date streaming data, such as current weather and traffic conditions.
Specialized identity data for understanding subscribers and audience targets.
New insights from unexpected sources of data.
The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on
Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft
Azure Government. Support for Microsoft Azure Government is planned.
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
A, B, C, D
Explanation:
As a consumer, you can do the following:
· Discover and test third-party data sources.
· Receive frictionless access to raw data products from vendors.
· Combine new datasets with your existing data in Snowflake to derive new business insights.
· Have datasets available instantly and updated continually for users.
· Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date
data.
· Use the business intelligence (BI) tools of your choice.
Which one is the incorrect option to share data in Snowflake?
B
Explanation:
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
· a Listing, in which you offer a share and additional metadata as a data product to one or more ac-
counts,
· a Direct Share, in which you directly share specific database objects (a share) to another account in
your region,
· a Data Exchange, in which you set up and manage a group of accounts and offer a share to that
group.
Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share us-
ing Which of the following options?
B, C
Explanation:
What is a Share?
Shares are named Snowflake objects that encapsulate all of the information required to share a
database.
Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share
using either or both of the following options:
Option 1: Grant privileges on objects to a share via a database role.
Option 2: Grant privileges on objects directly to a share.
You choose which accounts can consume data from the share by adding the accounts to the share.
After a database is created (in a consumer account) from a share, all the shared objects are
accessible to users in the consumer account.
Shares are secure, configurable, and controlled completely by the provider account:
· New objects added to a share become immediately available to all consumers, providing real-time
access to shared data.
Access to a share (or any of the objects in a share) can be revoked at any time.
Secure Data Sharing do not let you share which of the following selected objects in a database in your
account with other Snowflake accounts?
A
Explanation:
Secure Data Sharing lets you share selected objects in a database in your account with other Snow-
flake accounts. You can share the following Snowflake database objects:
Tables
External tables
Secure views
Secure materialized views
Secure UDFs
Snowflake enables the sharing of databases through shares, which are created by data providers and
“imported” by data consumers.