SlideShare a Scribd company logo
1 of 6
Download to read offline
supervised and
unsupervised learning
Submitted by-
Paras Kohli
B.Tech (CSE)
Supervised learning
• Supervised learning:
suppose you had a basket and it is fulled with some fresh fruits your
task is to arrange the same type fruits at one place.
• suppose the fruits are apple,banana,cherry,grape.
• so you already know from your previous work that, the shape of each
and every fruit so it is easy to arrange the same type of fruits at one
place.
• here your previous work is called as train data in data mining.
• so you already learn the things from your train data, This is because
of you have a response variable which says you that if some fruit
have so and so features it is grape, like that for each and every fruit.
• This type of data you will get from the train data.
• This type of learning is called as supervised learning.
• This type solving problem come under Classification.
• So you already learn the things so you can do your job confidently.
Unsupervised learning
• suppose you had a basket and it is fulled with some fresh fruits
your task is to arrange the same type fruits at one place.
• This time you don't know any thing about that fruits, you are
first time seeing these fruits so how will you arrange the same
type of fruits.
• What you will do first you take on fruit and you will select any
physical character of that particular fruit. suppose you taken
colours.
• Then the groups will be some thing like this.
• RED COLOR GROUP: apples & cherry fruits.
GREEN COLOR AND SMALL SIZE: grapes.
• This type of learning is know unsupervised learning.
Aim Of Supervised Learning
• The aim of supervised, machine learning is to build a model
that makes predictions based on evidence in the presence of
uncertainty. As adaptive algorithms identify patterns in data, a
computer "learns" from the observations. When exposed to
more observations, the computer improves its predictive
performance.
Example
• suppose you want to predict whether someone
will have a heart attack within a year. You have a
set of data on previous patients, including age,
weight, height, blood pressure, etc. You know
whether the previous patients had heart attacks
within a year of their measurements. So, the
problem is combining all the existing data into a
model that can predict whether a new person
will have a heart attack within a year.
THANK YOU

More Related Content

What's hot

Classification and Regression
Classification and RegressionClassification and Regression
Classification and RegressionMegha Sharma
 
Data preprocessing using Machine Learning
Data  preprocessing using Machine Learning Data  preprocessing using Machine Learning
Data preprocessing using Machine Learning Gopal Sakarkar
 
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic ConceptsData Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic ConceptsSalah Amean
 
Supervised learning and unsupervised learning
Supervised learning and unsupervised learningSupervised learning and unsupervised learning
Supervised learning and unsupervised learningArunakumariAkula1
 
Machine learning algorithms
Machine learning algorithmsMachine learning algorithms
Machine learning algorithmsShalitha Suranga
 
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
 
Machine learning Algorithms
Machine learning AlgorithmsMachine learning Algorithms
Machine learning AlgorithmsWalaa Hamdy Assy
 
Decision trees in Machine Learning
Decision trees in Machine Learning Decision trees in Machine Learning
Decision trees in Machine Learning Mohammad Junaid Khan
 
Unsupervised learning (clustering)
Unsupervised learning (clustering)Unsupervised learning (clustering)
Unsupervised learning (clustering)Pravinkumar Landge
 
Classification in data mining
Classification in data mining Classification in data mining
Classification in data mining Sulman Ahmed
 
Deep neural networks
Deep neural networksDeep neural networks
Deep neural networksSi Haem
 
Classification Based Machine Learning Algorithms
Classification Based Machine Learning AlgorithmsClassification Based Machine Learning Algorithms
Classification Based Machine Learning AlgorithmsMd. Main Uddin Rony
 
Supervised and Unsupervised Machine Learning
Supervised and Unsupervised Machine LearningSupervised and Unsupervised Machine Learning
Supervised and Unsupervised Machine LearningSpotle.ai
 
Machine Learning - Dataset Preparation
Machine Learning - Dataset PreparationMachine Learning - Dataset Preparation
Machine Learning - Dataset PreparationAndrew Ferlitsch
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.pptbutest
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
 

What's hot (20)

Classification and Regression
Classification and RegressionClassification and Regression
Classification and Regression
 
Data preprocessing using Machine Learning
Data  preprocessing using Machine Learning Data  preprocessing using Machine Learning
Data preprocessing using Machine Learning
 
Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Support Vector Machines ( SVM )
Support Vector Machines ( SVM )
 
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic ConceptsData Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
 
Supervised learning and unsupervised learning
Supervised learning and unsupervised learningSupervised learning and unsupervised learning
Supervised learning and unsupervised learning
 
Machine learning algorithms
Machine learning algorithmsMachine learning algorithms
Machine learning algorithms
 
Machine learning
Machine learningMachine learning
Machine learning
 
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
 
supervised learning
supervised learningsupervised learning
supervised learning
 
Machine learning Algorithms
Machine learning AlgorithmsMachine learning Algorithms
Machine learning Algorithms
 
Decision trees in Machine Learning
Decision trees in Machine Learning Decision trees in Machine Learning
Decision trees in Machine Learning
 
Unsupervised learning (clustering)
Unsupervised learning (clustering)Unsupervised learning (clustering)
Unsupervised learning (clustering)
 
Classification in data mining
Classification in data mining Classification in data mining
Classification in data mining
 
Deep neural networks
Deep neural networksDeep neural networks
Deep neural networks
 
Classification Based Machine Learning Algorithms
Classification Based Machine Learning AlgorithmsClassification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
 
Supervised and Unsupervised Machine Learning
Supervised and Unsupervised Machine LearningSupervised and Unsupervised Machine Learning
Supervised and Unsupervised Machine Learning
 
Machine Learning - Dataset Preparation
Machine Learning - Dataset PreparationMachine Learning - Dataset Preparation
Machine Learning - Dataset Preparation
 
MachineLearning.ppt
MachineLearning.pptMachineLearning.ppt
MachineLearning.ppt
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
 
Data Mining: Association Rules Basics
Data Mining: Association Rules BasicsData Mining: Association Rules Basics
Data Mining: Association Rules Basics
 

Similar to Supervised and unsupervised learning

Machine Learning By Bohair Naseer
Machine Learning  By Bohair NaseerMachine Learning  By Bohair Naseer
Machine Learning By Bohair NaseerBOHAIR-NS
 
Machine Learning By Bohair Naseer
Machine Learning  By Bohair NaseerMachine Learning  By Bohair Naseer
Machine Learning By Bohair NaseerBOHAIR-NS
 
Anxiety an obstacle to learning
Anxiety an obstacle to learningAnxiety an obstacle to learning
Anxiety an obstacle to learningDavid Krasky
 
Anxiety an obstacle to learning
Anxiety an obstacle to learningAnxiety an obstacle to learning
Anxiety an obstacle to learningDavid Krasky
 
Mastering mid terms
Mastering mid termsMastering mid terms
Mastering mid termsGameChanger3
 
Effective Use of Surveys in UX | Triangle UXPA Workshop
Effective Use of Surveys in UX | Triangle UXPA WorkshopEffective Use of Surveys in UX | Triangle UXPA Workshop
Effective Use of Surveys in UX | Triangle UXPA WorkshopAmanda Stockwell
 
Scientific method ppt
Scientific method pptScientific method ppt
Scientific method pptMarie Miller
 
Whose behavior is it anyway
Whose behavior is it anywayWhose behavior is it anyway
Whose behavior is it anywaymissymal
 
Level 5 Subjects within the Curriculum Session 2
Level 5 Subjects within the Curriculum Session 2 Level 5 Subjects within the Curriculum Session 2
Level 5 Subjects within the Curriculum Session 2 MariaElsam
 
thinking skills and research-1
thinking skills and research-1thinking skills and research-1
thinking skills and research-1KL Woon
 
What is GREAT teaching and learning? By the staff at Chalfonts.
What is GREAT teaching and learning? By the staff at Chalfonts.What is GREAT teaching and learning? By the staff at Chalfonts.
What is GREAT teaching and learning? By the staff at Chalfonts.MrsMcGinty
 
Evidence based practices(what are they)
Evidence based practices(what are they)Evidence based practices(what are they)
Evidence based practices(what are they)Melissa Fee
 
Maths study skills dfs-edc
Maths study skills dfs-edcMaths study skills dfs-edc
Maths study skills dfs-edcFarhana Shaheen
 
Write Better First Drafts - Grammar_David Marsh
Write Better First Drafts - Grammar_David MarshWrite Better First Drafts - Grammar_David Marsh
Write Better First Drafts - Grammar_David MarshCORE Group
 

Similar to Supervised and unsupervised learning (20)

Machine Learning By Bohair Naseer
Machine Learning  By Bohair NaseerMachine Learning  By Bohair Naseer
Machine Learning By Bohair Naseer
 
Machine Learning By Bohair Naseer
Machine Learning  By Bohair NaseerMachine Learning  By Bohair Naseer
Machine Learning By Bohair Naseer
 
A little about data mining
A little about data miningA little about data mining
A little about data mining
 
Anxiety an obstacle to learning
Anxiety an obstacle to learningAnxiety an obstacle to learning
Anxiety an obstacle to learning
 
Anxiety an obstacle to learning
Anxiety an obstacle to learningAnxiety an obstacle to learning
Anxiety an obstacle to learning
 
Activity slides
Activity slidesActivity slides
Activity slides
 
Mastering mid terms
Mastering mid termsMastering mid terms
Mastering mid terms
 
Effective Use of Surveys in UX | Triangle UXPA Workshop
Effective Use of Surveys in UX | Triangle UXPA WorkshopEffective Use of Surveys in UX | Triangle UXPA Workshop
Effective Use of Surveys in UX | Triangle UXPA Workshop
 
Using Data to Drive Instruction
Using Data to Drive InstructionUsing Data to Drive Instruction
Using Data to Drive Instruction
 
ABA
ABAABA
ABA
 
Scientific method ppt
Scientific method pptScientific method ppt
Scientific method ppt
 
Whose behavior is it anyway
Whose behavior is it anywayWhose behavior is it anyway
Whose behavior is it anyway
 
Level 5 Subjects within the Curriculum Session 2
Level 5 Subjects within the Curriculum Session 2 Level 5 Subjects within the Curriculum Session 2
Level 5 Subjects within the Curriculum Session 2
 
thinking skills and research-1
thinking skills and research-1thinking skills and research-1
thinking skills and research-1
 
What is GREAT teaching and learning? By the staff at Chalfonts.
What is GREAT teaching and learning? By the staff at Chalfonts.What is GREAT teaching and learning? By the staff at Chalfonts.
What is GREAT teaching and learning? By the staff at Chalfonts.
 
Evidence based practices(what are they)
Evidence based practices(what are they)Evidence based practices(what are they)
Evidence based practices(what are they)
 
Essential questions
Essential questionsEssential questions
Essential questions
 
study tips for students
study tips for studentsstudy tips for students
study tips for students
 
Maths study skills dfs-edc
Maths study skills dfs-edcMaths study skills dfs-edc
Maths study skills dfs-edc
 
Write Better First Drafts - Grammar_David Marsh
Write Better First Drafts - Grammar_David MarshWrite Better First Drafts - Grammar_David Marsh
Write Better First Drafts - Grammar_David Marsh
 

Recently uploaded

BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...Nguyen Thanh Tu Collection
 
DiskStorage_BasicFileStructuresandHashing.pdf
DiskStorage_BasicFileStructuresandHashing.pdfDiskStorage_BasicFileStructuresandHashing.pdf
DiskStorage_BasicFileStructuresandHashing.pdfChristalin Nelson
 
PART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFE
PART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFEPART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFE
PART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFEMISSRITIMABIOLOGYEXP
 
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxCLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxAnupam32727
 
ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6Vanessa Camilleri
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWQuiz Club NITW
 
Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...
Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...
Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...DrVipulVKapoor
 
Unit :1 Basics of Professional Intelligence
Unit :1 Basics of Professional IntelligenceUnit :1 Basics of Professional Intelligence
Unit :1 Basics of Professional IntelligenceDr Vijay Vishwakarma
 
Scientific Writing :Research Discourse
Scientific  Writing :Research  DiscourseScientific  Writing :Research  Discourse
Scientific Writing :Research DiscourseAnita GoswamiGiri
 
Shark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristicsShark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristicsArubSultan
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDhatriParmar
 
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...Association for Project Management
 
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...DhatriParmar
 
BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...
BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...
BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...Nguyen Thanh Tu Collection
 
DBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdfDBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdfChristalin Nelson
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...Nguyen Thanh Tu Collection
 
Objectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptxObjectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptxMadhavi Dharankar
 

Recently uploaded (20)

BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (GLOB...
 
DiskStorage_BasicFileStructuresandHashing.pdf
DiskStorage_BasicFileStructuresandHashing.pdfDiskStorage_BasicFileStructuresandHashing.pdf
DiskStorage_BasicFileStructuresandHashing.pdf
 
Chi-Square Test Non Parametric Test Categorical Variable
Chi-Square Test Non Parametric Test Categorical VariableChi-Square Test Non Parametric Test Categorical Variable
Chi-Square Test Non Parametric Test Categorical Variable
 
PART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFE
PART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFEPART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFE
PART 1 - CHAPTER 1 - CELL THE FUNDAMENTAL UNIT OF LIFE
 
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptxCLASSIFICATION OF ANTI - CANCER DRUGS.pptx
CLASSIFICATION OF ANTI - CANCER DRUGS.pptx
 
ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6ICS 2208 Lecture Slide Notes for Topic 6
ICS 2208 Lecture Slide Notes for Topic 6
 
Mythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITWMythology Quiz-4th April 2024, Quiz Club NITW
Mythology Quiz-4th April 2024, Quiz Club NITW
 
Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...
Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...
Geoffrey Chaucer Works II UGC NET JRF TGT PGT MA PHD Entrance Exam II History...
 
Unit :1 Basics of Professional Intelligence
Unit :1 Basics of Professional IntelligenceUnit :1 Basics of Professional Intelligence
Unit :1 Basics of Professional Intelligence
 
Scientific Writing :Research Discourse
Scientific  Writing :Research  DiscourseScientific  Writing :Research  Discourse
Scientific Writing :Research Discourse
 
Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...
Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...
Plagiarism,forms,understand about plagiarism,avoid plagiarism,key significanc...
 
Shark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristicsShark introduction Morphology and its behaviour characteristics
Shark introduction Morphology and its behaviour characteristics
 
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptxDecoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
Decoding the Tweet _ Practical Criticism in the Age of Hashtag.pptx
 
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
Team Lead Succeed – Helping you and your team achieve high-performance teamwo...
 
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
Beauty Amidst the Bytes_ Unearthing Unexpected Advantages of the Digital Wast...
 
BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...
BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...
BÀI TẬP BỔ TRỢ 4 KĨ NĂNG TIẾNG ANH LỚP 8 - CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC ...
 
DBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdfDBMSArchitecture_QueryProcessingandOptimization.pdf
DBMSArchitecture_QueryProcessingandOptimization.pdf
 
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 - I-LEARN SMART WORLD - CẢ NĂM - CÓ FILE NGHE (BẢN...
 
Objectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptxObjectives n learning outcoms - MD 20240404.pptx
Objectives n learning outcoms - MD 20240404.pptx
 
Introduction to Research ,Need for research, Need for design of Experiments, ...
Introduction to Research ,Need for research, Need for design of Experiments, ...Introduction to Research ,Need for research, Need for design of Experiments, ...
Introduction to Research ,Need for research, Need for design of Experiments, ...
 

Supervised and unsupervised learning

  • 1. supervised and unsupervised learning Submitted by- Paras Kohli B.Tech (CSE)
  • 2. Supervised learning • Supervised learning: suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place. • suppose the fruits are apple,banana,cherry,grape. • so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place. • here your previous work is called as train data in data mining. • so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit. • This type of data you will get from the train data. • This type of learning is called as supervised learning. • This type solving problem come under Classification. • So you already learn the things so you can do your job confidently.
  • 3. Unsupervised learning • suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place. • This time you don't know any thing about that fruits, you are first time seeing these fruits so how will you arrange the same type of fruits. • What you will do first you take on fruit and you will select any physical character of that particular fruit. suppose you taken colours. • Then the groups will be some thing like this. • RED COLOR GROUP: apples & cherry fruits. GREEN COLOR AND SMALL SIZE: grapes. • This type of learning is know unsupervised learning.
  • 4. Aim Of Supervised Learning • The aim of supervised, machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. As adaptive algorithms identify patterns in data, a computer "learns" from the observations. When exposed to more observations, the computer improves its predictive performance.
  • 5. Example • suppose you want to predict whether someone will have a heart attack within a year. You have a set of data on previous patients, including age, weight, height, blood pressure, etc. You know whether the previous patients had heart attacks within a year of their measurements. So, the problem is combining all the existing data into a model that can predict whether a new person will have a heart attack within a year.