MODULE 1

Question 1

Algorithm where samples are used for training.

Answer 1

Machine Learning Algorithm

Question 2

It is a research field at the intersection of statistics, artificial intelligence, and computer science.

Answer 2

Machine Learning

Question 3

It is the practice of cleaning, altering, and reorganizing raw data prior to processing and analysis

Answer 3

Preprocessing

Question 4

contains inconsistent records

Answer 4

Inconsistency

Question 5

contains incorrect records or exceptions

Answer 5

Noise

Question 6

Creating plots and charts to visualize data distributions and relationships.

Answer 6

Visualization

Question 7

T/F
The performance of ML algorithms adaptively improves with an increase in the number of available samples during the ‘training’ processes.

Answer 7

FALSE: (‘learning’)

Question 8

T/F
Data reduction is a data cleansing technique.

Answer 8

FALSE

Question 9

T/F
Reducing noise in data is a feature engineering technique.

Answer 9

FALSE

Question 10

It covers the ethical and moral obligations of collecting, sharing, and using data, focused on ensuring that data is used fairly, for good.

Answer 10

Data Ethics

Question 11

Best Practices for Successful ML Model Deployment

Answer 11

1. Choosing the Right Infrastructure.
2. Effective Versioning and Tracking
3. Robust Testing and Validation
4. Implementing Monitoring and Alerting

Question 12

Data: _____________
Learning Algorithms: ______________
Basic Understanding: ______________

Answer 12

Experience (E)
Task (T)
Measure (P)

Question 13

Even when intentions are good, the ___________ of data analysis can cause inadvertent harm to individuals or groups of people.

Answer 13

Outcome

Question 14

Once deployed, models need to be continuously monitored.

Answer 14

Monitoring

Question 15

A field of study concerned with giving computers the ability to learn without being explicitly programmed.

Answer 15

Machine Learning

Question 16

It is a collection of data used in machine learning tasks.

Answer 16

Dataset

Question 17

Feature Engineering techniques

Answer 17

• Feature scaling or normalization
• Data reduction
• Discretization
• Feature encoding

Question 18

Calculating measures like mean, median, variance, and standard deviation.

Answer 18

Summary Statistics

Question 19

Data Cleansing techniques

Answer 19

• Identify and sort out missing data
• Reduce noisy data
• Identify and remove duplicates

Question 20

It is used to understand the main characteristics of the data, identify patterns to discover patterns, spot anomalies, test a hypothesis, or check assumptions.

Answer 20

Exploratory Data Analysis (EDA)

Question 21

The process of creating a model from data is called ___________

Answer 21

Learning (training)

Question 22

Rule-based algorithms: Condition
Machine Learning: _________.

Answer 22

Model

Question 23

Algorithm where explicit programming is used.

Answer 23

Rule Based Algorithm

Question 24

It refers to the process of using the model obtained after learning for prediction.

Answer 24

Testing

Question 25

It is the most crucial process of integrating the ML model into its production environment. This process is the most challenging, involving several moving pieces, tools, data scientists, and ML engineers to collaborate and strategize.

Answer 25

Model Deployment

Question 26

In addition to owning their personal information, data subjects have a right to know how you plan to collect, store, and use it.

Answer 26

Transparency

Question 27

Another ethical responsibility that comes with handling data is ensuring data subjects’ ____________

Answer 27

Privacy

Question 28

Machine Learning is a field of study concerned with giving computers the ability to ________ without being explicitly programmed.

Answer 28

Learn

Question 29

___________ matter. Before collecting data, ask yourself why you need it, what you’ll gain from it, and what changes you’ll be able to make after analysis

Answer 29

Intention

Question 30

Data Preprocessing Techniques

Answer 30

Data Cleansing Feature Engineering

Question 31

Machine Learning Workflow

Answer 31

1. Project Setup 2. Data Preparation 3. Modelling 4. Deployment

Question 32

Before deployment, models need to be thoroughly trained and evaluated. This involves data preprocessing, feature engineering, and rigorous testing to ensure the model is robust and ready for real-world scenarios.

Answer 32

Training

Question 33

EDA Activities

Answer 33

Visualization Summary Statistics Outlier Detection Correlation Analysis Hypothesis Testing

Question 34

contains missing values or the data that lacks attributes

Answer 34

Incompleteness

Question 35

It is a discipline of artificial intelligence (AI) that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention.

Answer 35

Machine Learning

Question 36

Which data preprocessing task is the most time consuming?

Answer 36

Data cleaning

Question 37

Phases of ML

Answer 37

1. Learning 2. Prediction

Question 38

5 Principles of Data Ethics

Answer 38

Ownership Transparency Privacy Intention Outcomes

Question 39

It is an observation that seems to be distant from other observations.

Answer 39

Outlier

Question 40

Without good ________, there is no good _________.

Answer 40

data, model

Question 41

Algorithm where the decision-making rules are complex and difficult to describe.

Answer 41

Machine Learning Algorithm

Question 42

Algorithm where rules are automatically learned by the machines.

Answer 42

Machine Learning Algorithm

Question 43

Examining relationships between variables.

Answer 43

Correlation Analysis

Question 44

Important step before processing

Answer 44

To prepare the data for analysis or modeling by cleaning and transforming it.

Question 45

A continuous data is:

Answer 45

Quantitative

Question 46

T/F Machines driven by algorithms designed by humans are able to learn latent rules and inherent patterns and to fulfill tasks desired by humans.

Answer 46

TRUE

Question 47

Steps for Data Preprocessing

Answer 47

1. Data Profiling 2. Data Cleansing 3. Data Reduction 4. Data Transformation 5. Data Enrichment 6. Data Validation

Question 48

Events or attributes that reflect the performance or nature of a sample in a particular aspect are called ____________

Answer 48

Features

Question 49

It is a dataset used in the training process, where each sample is referred to as a training sample.

Answer 49

Training set

Question 50

Also known as predictive analytics or statistical learning.

Answer 50

Machine Learning

Question 51

Rule Complexity : Scale of the Problem Simple : Small = ____________ Complex: Small = ____________ Simple : Large = _____________ Complex : Large = ____________

Answer 51

Simple Problems Manual Rules Rule Based Algorithm Machine Learning Algorithm

Question 52

Algorithm where rules can be specified.

Answer 52

Rule Based Algorithm

Question 53

Each data record is called a __________

Answer 53

Sample

Question 54

Testing initial assumptions about the data.

Answer 54

Hypothesis Testing

Question 55

It is about extracting knowledge from data.

Answer 55

Machine Learning

Question 56

Data Preprocessing is also called as ___________

Answer 56

Data Preparation

Question 57

T/F Machine Learning methods enable computers to operate autonomously without explicit programming.

Answer 57

TRUE

Question 58

It refers to the process of taking a trained ML model and making it available for use in real-world applications

Answer 58

Machine Learning Model Deployment

Question 59

Identifying unusual data points.

Answer 59

Outlier Detection

Question 60

T/F Sorting out missing data is a data cleansing technique.

Answer 60

TRUE

Question 61

It is a set of policies, procedures, and standards that implements data governance of an organization.

Answer 61

Data Governance Framework

Question 62

Types of Machine Learning

Answer 62

Supervised Unsupervised Semi-Supervised Reinforcement

Question 63

It is a study of learning algorithms.

Answer 63

Machine Learning

Question 64

It is a set of principles and processes for data collection, management, and use. The goal is to ensure that data is accurate, consistent, and available for use, while protecting data privacy and security.

Answer 64

Data Governance

Question 65

A nominal data is:

Answer 65

Qualitative

Question 66

ML models should be able to handle increased loads and continue to deliver results efficiently. Ensuring the infrastructure can handle the model's computational requirements is vital, requiring validation and effective testing for scalability before deploying models.

Answer 66

Validation

Question 67

The first principle of data ethics is that an individual has __________ over their personal information.

Answer 67

Ownership

Question 68

Machine Learning Deployment Model

Answer 68

Training Validation Deployment Monitoring