Intro

Question 1

Machine learning?

Answer 1

Machine learning is programming a computer to optimize criterion using sample data or past experience.

Question 2

Machine learning is??

Answer 2

“A computer program is said to learn from experience E w.r.t some task T and some performance evaluation measures P, if its performance P on a task T improves with experience E.”

Question 3

Suppose your email program watches which email you do or do not mark as spam, and based on that learns how to better filter spam. What is task T in this setting?

Answer 3

1) Classifying email as spam or not.
2) Watching you label email as spam or not.
3) The number of fractions correctly marked as spam or not.

Question 4

Before ML, applications:-

Answer 4

1) Data Cleaning
2) Data Pre-processing
3) Data transformation/Normalization
4) Data Integration
5) Multidimensional data issues

Question 5

Data Cleaning

Answer 5

Data cleansing or data cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

Question 6

Data Pre-processing

Answer 6

Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. Data preprocessing prepares raw data for further processing.

examples:Data preprocessing is used database-driven applications such as customer relationship management and rule-based applications (like neural networks).

Question 7

Data transformation/Normalization

Answer 7

Data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration.

Question 8

Data Integration

Answer 8

Data integration is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation.

Question 9

Multidimensional data issues

Answer 9

In statistics, econometrics, and related fields, multidimensional analysis (MDA) is a data analysis process that groups data into two categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a single-dimensional (in this case, longitudinal) data set. A data set consisting of the number of wins for several football teams in a single year is also a single-dimensional (in this case, cross-sectional) data set. A data set consisting of the number of wins for several football teams over several years is a two-dimensional data set.

Question 10

Association rule mining

Answer 10

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness

Question 11

basket analysis

Answer 11

Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

Question 12

method of association rule mining

Answer 12

aproiri algorithm

Question 13

frequent patterns

Answer 13

What items are frequently purchased together in your SaveMart?
A typical association rule
P(Milk | Bread) = 0.7

Question 14

types of learning

Answer 14

1) supersvised

2) unsupervised

Question 15

supersvised

Answer 15

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

Question 16

unsupervised learning

Answer 16

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Question 17

Classification and Label Prediction

Answer 17

Construct models based on some training examples
Describe and distinguish classes or concepts for future prediction
Predict some unknown class labels

Question 18

Typical Methods

of classification

Answer 18

Decision trees, Naïve Bayes, SVM, Neural Networks

Question 19

Applications of classifications

Answer 19

Sentiment Classification
Spam Classification
Disease Prediction
Match result prediction

Question 20

Credit Card Approval

Answer 20

example of classification.
A credit card company receives thousands of applications for new cards. Each application contains information about applicant,
Age
Marital status
Annual salary
Outstanding debts
Credit rating 
etc.
Problem: to decide whether an application should approved or not.

Question 21

Loan Application Example

Answer 21

classification example

Question 22

Regression

Answer 22

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables, they are considering and the number of independent variables being used.

Question 23

regression example

Answer 23

Predicting price of house when area is given.

Question 24

Clustering

Answer 24

Unsupervised learning
Class label is unknown
Group data to form clusters

Question 25

Clustering principle

Answer 25

Maximize inter-class difference and minimize intra-class difference

Question 26

Methods

of clustering

Answer 26

K-Means Clustering

Cobweb

Question 27

Applications

of clustering

Answer 27

Document clustering

Groups or similar people finding

Question 28

Outlier Detection

Answer 28

In data mining, anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data

Question 29

Outlier analysis

Answer 29

Also known as Anomaly Detection

Outlier is a data object that does not comply with the general behavior of the data.

Question 30

Applications

of outlier detection

Answer 30

Rare event analysis

Fraud detection

Question 31

Why Machine Learning?

Answer 31

The Explosive growth of data: from terabytes to petabytes

TB = 1024 GB, PB = 1024 TB

Question 32

Why Machine Learning?Major source of data

Answer 32

Business: web, e-commerce, transactions, stocks …
Science : Remote sensing, bio-informatics
……
Society : news, YouTube, digital cameras

Question 33

process a typical view of ML

Answer 33

INPUT ->>
DATA PREPROCESSING->>
MACHINE LEARNING->>
POST PROCESSING->>
PATTERN INFORMATION KNOWLEDGE

Question 34

DATA PREPROCESSING

Answer 34

1) DATA INTEGRATION
2) NORMALIZATION
3) FEATURE SELECTION
4) DIMENSION REDUCTION

Question 35

MACHINE LEARNING

Answer 35

1) PATTERN DISCOVERY
2) ASSOCIATION AND CORRELATION
3) CLASSIFICATION
4) CLUSTERING
4) OUTLIER ANALYSIS

Question 36

POST PROCESSING

Answer 36

1) PATTERN EVALUATION
2) PATTERN SELECTION
3) PATTERN INTERPRETATION
4) PATTERN VISUALIZATION