FA1 - Machine Learning

ML provides machines the ability to automatically learn from data while identifying patterns to make ______ with minimal human intervention. 

predictions

Types of Machine Learning : Supervised, Unsupervised, Semi-Supervised, and __________. 

reinforcement

Letters, Symbols, Words, Gender are samples of:

Ordinal data

Nominal data

Discrete data

Continuous data

Nominal data

The act of filling in missing values by estimation.

Imputation

Mean Imputation

Most-frequent Imputation

Column Transformation

Imputation

A categorical data is:

Qualitative

Number of students in the class is a sample of:

Discrete data

The ______ is an observation that goes far outside the average value of a group of statistics.

outlier

Based on the ML application table scenario, when rule gets complex and problem scale is small, ML application is:

Rule-base Algorithms

Simple Problem

ML Algorithms

Manual Rules

Manual Rules

Based on the ML application table scenario, when rule complexity is simple and problem scale is large, ML application is:

Rule-based Algorithms

Manual Rules

ML Algorithms

Simple Prolem

Rule-based Algorithms

Which data preprocessing task is the most time consuming?

Data modeling

Data analysis

Data collection

Data cleaning

Data cleaning

A continuous data is:

Quantitative

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

learn

Which is not true about Machine Learning?

Enable computers to operate autonomously with explicit programming.

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

Their maintenance is much lower than a human's and costs a lot less in the long run.

Automation by machine learning can mitigate risks caused by fatigue or inattention.

Enable computers to operate autonomously with explicit programming.

Which processes are involved in data preparation?

Data collection, Data Cleaning

Not in the options

Data Cleaning, Feature Engineering

All the given options

Splitting of dataset

All the given options

Rule-based algorithms: Condition 

Machine Learning: _________. 

model

Removing duplicates in the dataset is a feature engineering technique. (T or F)

False

An ordinal data is:

Qualitative

Data reduction is a feature engineering technique. (T or F)

True

Choose all the most popular Python Libraries that are used in data science.

PANDAS

NUMPY

JUPYTER

SCIPY

SQL

ANACONDA

PANDAS

NUMPY

SCIPY

Sorting out missing data is a data cleansing technique. (T or F)

True

Dataset is divided into _______ set and test set. 

train

Based on the ML application table scenario, when rule complexity is complex and problem scale is large, ML application is:

Simple Problem

Rule-based Algorithms

ML Algorithms

Manual Rules

ML Algorithms

What are the two main phases of ML workflow?

Training, Modeling

Training, Testing

Learning, Prediction

Learning, Modeling

Training, Prediction

Learning, Prediction

In EDA, this process identifies unusual data points. _________

outlier detection

Reducing noise in data is a feature engineering technique. (T or F)

False

Temperature range is a sample of:

Continuous Data

Data reduction is a data cleansing technique. (T or F)

False

The ______ analysis examines relationship between variables.

correlation

Which is true about ML, AI, and DS?

DS and AI are subsets of ML

All the given options

DS and ML are subsets of AI

AI and ML are subsets of DS

AI and ML are subsets of DS

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

learn

This dataset is used in the process of using the model obtained after learning for prediction.

Test Set

Training and Test Set

Model Set

Training set

Test Set

ML is a research field at the intersection of _________, artificial intelligence, and computer science. 

statistics

A nominal data is:

Qualitative

Movie ratings, Military rank are samples of:

Continuous data

Discrete data

Ordinal data

Nominal data

Ordinal Data (Wrong Canvas)