SlideShare a Scribd company logo

Supervised Machine Learning

•Download as PPTX, PDF•
•
Livares Technologies Pvt Ltd
Livares Technologies Pvt Ltd

Supervised machine learning uses labeled training data to build models that can predict outputs. There are two main types: regression predicts continuous variables, while classification predicts categorical variables. Supervised learning algorithms include linear regression, which finds a linear relationship between variables, and logistic regression or decision trees for classification. The process involves collecting labeled data, training an algorithm on part of the data, and evaluating its accuracy on test data.

Technology
1 of 20
SUPERVISED MACHINE LEARNING Presented By Amal Mohanan Livares Technologies Pvt Ltd Tech&Socio-Cultural Group
Machine learning Continue … Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Types of Machine Learning Continue… ⮚ Supervised Machine Learning ⮚ Unsupervised Machine Learning Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
What is Supervised Learning? Continue… Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
How Supervised Learning Works? Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
What is Unsupervised Learning? Continue… Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Working of Unsupervised Learning Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Steps Involved in Supervised Learning Continue… ⮚ Determine the type of training dataset ⮚ Collect/Gather the labelled training data. ⮚ Split the training dataset into training dataset, test dataset, ⮚ Determine the suitable algorithm for the model ⮚ Execute the algorithm on the training dataset. ⮚ Evaluate the accuracy of the model by providing the test set. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Types of Supervised Learning Algorithms Continue… ⮚ Regression Analysis Regression analysis is a statistical method to model the relationship between a dependent (target) and independent (predictor) variables with one or more independent variables. ⮚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. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Types Regression Analysis Continue… ⮚ Linear Regression Linear relationship between dependent and independent variables ⮚ Polynomial Regression Non linear relationship between dependent and independent variables Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Types Regression Analysis Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Linear Regression Continue… Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (x) variables, hence called as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Assumptions of Linear Regression Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Types of Linear Regression Analysis Continue… ▪ Simple Linear Regression ▪ Multiple Linear Regression Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Simple Linear Regression Continue… Simple Linear Regression is a type of Regression algorithms that models the relationship between a dependent variable and a single independent variable. The relationship shown by a Simple Linear Regression model is linear or a sloped straight line, hence it is called Simple Linear Regression. Equation y = b0 + b1x b0 = It is the intercept of the Regression line b1 = It is the slope of the regression line, which tells whether the line is increasing or decreasing. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Multiple Linear Regression Continue… Multiple Linear Regression is one of the important regression algorithms which models the linear relationship between a single dependent continuous variable and more than one independent variable. Equation y = b0 + b1x1 + b2x2 + b3x3 +….. b0,b1.b2,…. = Coefficients of the model x1, x2, x3, x4,...= Various Independent/feature variable Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Backward Elimination Continue… Backward elimination is a feature selection technique while building a machine learning model. It is used to remove those features that do not have a significant effect on the dependent variable or prediction of output. There are various ways to build a model in Machine Learning, which are: ⮚ All-in ⮚ Backward Elimination ⮚ Forward Selection ⮚ Bidirectional Elimination ⮚ Score Comparison Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Steps of Backward Elimination Continue… Step-1: Firstly, We need to select a significance level to stay in the model. (SL=0.05) Step-2: Fit the complete model with all possible predictors/independent variables Step-3: Choose the predictor which has the highest P-value, such that. 1. If P-value >SL, go to step 4. 2.Else Finish, and Our model is ready. Step-4: Remove that predictor. Step-5: Rebuild and fit the model with the remaining variables. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
Reference https://www.javatpoint.com/supervised-machine-learning Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
OUR CONTACT DETAILS Livares Technologies Pvt Ltd 5th Floor, Yamuna Building Technopark Phase III Campus Trivandrum, Kerala, India- 695581 Livares Technologies Pvt Ltd Tech&Socio-Cultural Group Our helpline is always open to receive any inquiry or feedback.Please feel free to contact us www.livares.com contact@livares.com @livaresofficial www.facebook.com/livaresofficial +91-471-2710003 | +91-471- 2710004 THANK YOU

Recommended

Machine Learning
Machine LearningMachine Learning
Machine Learning
Rahul Kumar
 
Machine learning
Machine learningMachine learning
Machine learning
ADARSHMISHRA126
 
Machine learning
Machine learningMachine learning
Machine learning
Rajib Kumar De
 
Supervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And TechniquesSupervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And Techniques
SlideTeam
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
Kumar P
 
Machine learning
Machine learningMachine learning
Machine learning
Dr Geetha Mohan
 
supervised learning
supervised learningsupervised learning
supervised learning
Amar Tripathi
 
Applications of Machine Learning
Applications of Machine LearningApplications of Machine Learning
Applications of Machine Learning
Hayim Makabee
 

Recommended

Machine Learning
Machine LearningMachine Learning
Machine Learning
Rahul Kumar
 
Machine learning
Machine learningMachine learning
Machine learning
ADARSHMISHRA126
 
Machine learning
Machine learningMachine learning
Machine learning
Rajib Kumar De
 
Supervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And TechniquesSupervised Machine Learning With Types And Techniques
Supervised Machine Learning With Types And Techniques
SlideTeam
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
Kumar P
 
Machine learning
Machine learningMachine learning
Machine learning
Dr Geetha Mohan
 
supervised learning
supervised learningsupervised learning
supervised learning
Amar Tripathi
 
Applications of Machine Learning
Applications of Machine LearningApplications of Machine Learning
Applications of Machine Learning
Hayim Makabee
 
Lecture1 introduction to machine learning
Lecture1 introduction to machine learningLecture1 introduction to machine learning
Lecture1 introduction to machine learning
UmmeSalmaM1
 
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Simplilearn
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?
Marina Santini
 
Introduction to ML (Machine Learning)
Introduction to ML (Machine Learning)Introduction to ML (Machine Learning)
Introduction to ML (Machine Learning)
SwatiTripathi44
 
Machine Can Think
Machine Can ThinkMachine Can Think
Machine Can Think
Rahul Jaiman
 
Machine Learning with Decision trees
Machine Learning with Decision treesMachine Learning with Decision trees
Machine Learning with Decision trees
Knoldus Inc.
 
Supervised learning
Supervised learning Supervised learning
Supervised learning
Learnbay Datascience
 
Classification Based Machine Learning Algorithms
Classification Based Machine Learning AlgorithmsClassification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
Md. Main Uddin Rony
 
ID3 ALGORITHM
ID3 ALGORITHMID3 ALGORITHM
ID3 ALGORITHM
HARDIK SINGH
 
Hyperparameter Tuning
Hyperparameter TuningHyperparameter Tuning
Hyperparameter Tuning
Jon Lederman
 
K-Nearest Neighbor Classifier
K-Nearest Neighbor ClassifierK-Nearest Neighbor Classifier
K-Nearest Neighbor Classifier
Neha Kulkarni
 
Machine learning ppt
Machine learning ppt Machine learning ppt
Machine learning ppt
Poojamanic
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
Oswald Campesato
 
introduction to machin learning
introduction to machin learningintroduction to machin learning
introduction to machin learning
nilimapatel6
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine Learning
Samra Shahzadi
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
Shahar Cohen
 
Machine Learning SPPU Unit 1
Machine Learning SPPU Unit 1Machine Learning SPPU Unit 1
Machine Learning SPPU Unit 1
Amruta Aphale
 
Machine learning seminar ppt
Machine learning seminar pptMachine learning seminar ppt
Machine learning seminar ppt
RAHUL DANGWAL
 
Unsupervised learning
Unsupervised learningUnsupervised learning
Unsupervised learning
amalalhait
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
Vivek Garg
 
Regression and Artificial Neural Network in R
Regression and Artificial Neural Network in RRegression and Artificial Neural Network in R
Regression and Artificial Neural Network in R
Dr. Vaibhav Kumar
 
Machine Learning Course | Edureka
Machine Learning Course | EdurekaMachine Learning Course | Edureka
Machine Learning Course | Edureka
Edureka!
 

More Related Content

What's hot

Lecture1 introduction to machine learning
Lecture1 introduction to machine learningLecture1 introduction to machine learning
Lecture1 introduction to machine learning
UmmeSalmaM1
 
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Simplilearn
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?
Marina Santini
 
Introduction to ML (Machine Learning)
Introduction to ML (Machine Learning)Introduction to ML (Machine Learning)
Introduction to ML (Machine Learning)
SwatiTripathi44
 
Machine Can Think
Machine Can ThinkMachine Can Think
Machine Can Think
Rahul Jaiman
 
Machine Learning with Decision trees
Machine Learning with Decision treesMachine Learning with Decision trees
Machine Learning with Decision trees
Knoldus Inc.
 
Supervised learning
Supervised learning Supervised learning
Supervised learning
Learnbay Datascience
 
Classification Based Machine Learning Algorithms
Classification Based Machine Learning AlgorithmsClassification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
Md. Main Uddin Rony
 
ID3 ALGORITHM
ID3 ALGORITHMID3 ALGORITHM
ID3 ALGORITHM
HARDIK SINGH
 
Hyperparameter Tuning
Hyperparameter TuningHyperparameter Tuning
Hyperparameter Tuning
Jon Lederman
 
K-Nearest Neighbor Classifier
K-Nearest Neighbor ClassifierK-Nearest Neighbor Classifier
K-Nearest Neighbor Classifier
Neha Kulkarni
 
Machine learning ppt
Machine learning ppt Machine learning ppt
Machine learning ppt
Poojamanic
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
Oswald Campesato
 
introduction to machin learning
introduction to machin learningintroduction to machin learning
introduction to machin learning
nilimapatel6
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine Learning
Samra Shahzadi
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
Shahar Cohen
 
Machine Learning SPPU Unit 1
Machine Learning SPPU Unit 1Machine Learning SPPU Unit 1
Machine Learning SPPU Unit 1
Amruta Aphale
 
Machine learning seminar ppt
Machine learning seminar pptMachine learning seminar ppt
Machine learning seminar ppt
RAHUL DANGWAL
 
Unsupervised learning
Unsupervised learningUnsupervised learning
Unsupervised learning
amalalhait
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
Vivek Garg
 

What's hot (20)

Lecture1 introduction to machine learning
Lecture1 introduction to machine learningLecture1 introduction to machine learning
Lecture1 introduction to machine learning
 
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?
 
Introduction to ML (Machine Learning)
Introduction to ML (Machine Learning)Introduction to ML (Machine Learning)
Introduction to ML (Machine Learning)
 
Machine Can Think
Machine Can ThinkMachine Can Think
Machine Can Think
 
Machine Learning with Decision trees
Machine Learning with Decision treesMachine Learning with Decision trees
Machine Learning with Decision trees
 
Supervised learning
Supervised learning Supervised learning
Supervised learning
 
Classification Based Machine Learning Algorithms
Classification Based Machine Learning AlgorithmsClassification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
 
ID3 ALGORITHM
ID3 ALGORITHMID3 ALGORITHM
ID3 ALGORITHM
 
Hyperparameter Tuning
Hyperparameter TuningHyperparameter Tuning
Hyperparameter Tuning
 
K-Nearest Neighbor Classifier
K-Nearest Neighbor ClassifierK-Nearest Neighbor Classifier
K-Nearest Neighbor Classifier
 
Machine learning ppt
Machine learning ppt Machine learning ppt
Machine learning ppt
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
 
introduction to machin learning
introduction to machin learningintroduction to machin learning
introduction to machin learning
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine Learning
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 
Machine Learning SPPU Unit 1
Machine Learning SPPU Unit 1Machine Learning SPPU Unit 1
Machine Learning SPPU Unit 1
 
Machine learning seminar ppt
Machine learning seminar pptMachine learning seminar ppt
Machine learning seminar ppt
 
Unsupervised learning
Unsupervised learningUnsupervised learning
Unsupervised learning
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
 

Similar to Supervised Machine Learning

Regression and Artificial Neural Network in R
Regression and Artificial Neural Network in RRegression and Artificial Neural Network in R
Regression and Artificial Neural Network in R
Dr. Vaibhav Kumar
 
Machine Learning Course | Edureka
Machine Learning Course | EdurekaMachine Learning Course | Edureka
Machine Learning Course | Edureka
Edureka!
 
Machine learning
Machine learningMachine learning
Machine learning
Ayesha Ahsan khan
 
Unit 3 – AIML.pptx
Unit 3 – AIML.pptxUnit 3 – AIML.pptx
Unit 3 – AIML.pptx
hiblooms
 
Machine Learning by Rj
Machine Learning by RjMachine Learning by Rj
Machine Learning by Rj
Shree M.L.Kakadiya MCA mahila college, Amreli
 
Internshipppt.pptx
Internshipppt.pptxInternshipppt.pptx
Internshipppt.pptx
VishalKumarSingh645583
 
unit 1.2 supervised learning.pptx
unit 1.2 supervised learning.pptxunit 1.2 supervised learning.pptx
unit 1.2 supervised learning.pptx
Dr.Shweta
 
Kickstart ML.pptx
Kickstart ML.pptxKickstart ML.pptx
Kickstart ML.pptx
GDSCVJTI
 
Machine Learning with Python- Methods for Machine Learning.pptx
Machine Learning with Python- Methods for Machine Learning.pptxMachine Learning with Python- Methods for Machine Learning.pptx
Machine Learning with Python- Methods for Machine Learning.pptx
iaeronlineexm
 
Supervised learning techniques and applications
Supervised learning techniques and applicationsSupervised learning techniques and applications
Supervised learning techniques and applications
Benjaminlapid1
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
Bhupender Sharma
 
Industrial training ppt
Industrial training pptIndustrial training ppt
Industrial training ppt
HRJEETSINGH
 
What is Machine Learning.docx
What is Machine Learning.docxWhat is Machine Learning.docx
What is Machine Learning.docx
Surendra Gusain
 
Top 10 Most Important Interview Question and Answer on Machine Learning
Top 10 Most Important Interview Question and Answer on Machine LearningTop 10 Most Important Interview Question and Answer on Machine Learning
Top 10 Most Important Interview Question and Answer on Machine Learning
DucatNoida1
 
Lecture 1.pptx
Lecture 1.pptxLecture 1.pptx
Lecture 1.pptx
Makerere Unversity School of Public Health, Victoria University
 
Machine Learning Contents.pptx
Machine Learning Contents.pptxMachine Learning Contents.pptx
Machine Learning Contents.pptx
Naveenkushwaha18
 
Supervised Machine Learning.pptx
Supervised Machine Learning.pptxSupervised Machine Learning.pptx
Supervised Machine Learning.pptx
ChanduChandran6
 
Student Performance Predictor
Student Performance PredictorStudent Performance Predictor
Student Performance Predictor
IRJET Journal
 
ML Lec 1 (1).pptx
ML Lec 1 (1).pptxML Lec 1 (1).pptx
ML Lec 1 (1).pptx
MuhammadTalha278665
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
Sujith Jayaprakash
 

Similar to Supervised Machine Learning (20)

Regression and Artificial Neural Network in R
Regression and Artificial Neural Network in RRegression and Artificial Neural Network in R
Regression and Artificial Neural Network in R
 
Machine Learning Course | Edureka
Machine Learning Course | EdurekaMachine Learning Course | Edureka
Machine Learning Course | Edureka
 
Machine learning
Machine learningMachine learning
Machine learning
 
Unit 3 – AIML.pptx
Unit 3 – AIML.pptxUnit 3 – AIML.pptx
Unit 3 – AIML.pptx
 
Machine Learning by Rj
Machine Learning by RjMachine Learning by Rj
Machine Learning by Rj
 
Internshipppt.pptx
Internshipppt.pptxInternshipppt.pptx
Internshipppt.pptx
 
unit 1.2 supervised learning.pptx
unit 1.2 supervised learning.pptxunit 1.2 supervised learning.pptx
unit 1.2 supervised learning.pptx
 
Kickstart ML.pptx
Kickstart ML.pptxKickstart ML.pptx
Kickstart ML.pptx
 
Machine Learning with Python- Methods for Machine Learning.pptx
Machine Learning with Python- Methods for Machine Learning.pptxMachine Learning with Python- Methods for Machine Learning.pptx
Machine Learning with Python- Methods for Machine Learning.pptx
 
Supervised learning techniques and applications
Supervised learning techniques and applicationsSupervised learning techniques and applications
Supervised learning techniques and applications
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
 
Industrial training ppt
Industrial training pptIndustrial training ppt
Industrial training ppt
 
What is Machine Learning.docx
What is Machine Learning.docxWhat is Machine Learning.docx
What is Machine Learning.docx
 
Top 10 Most Important Interview Question and Answer on Machine Learning
Top 10 Most Important Interview Question and Answer on Machine LearningTop 10 Most Important Interview Question and Answer on Machine Learning
Top 10 Most Important Interview Question and Answer on Machine Learning
 
Lecture 1.pptx
Lecture 1.pptxLecture 1.pptx
Lecture 1.pptx
 
Machine Learning Contents.pptx
Machine Learning Contents.pptxMachine Learning Contents.pptx
Machine Learning Contents.pptx
 
Supervised Machine Learning.pptx
Supervised Machine Learning.pptxSupervised Machine Learning.pptx
Supervised Machine Learning.pptx
 
Student Performance Predictor
Student Performance PredictorStudent Performance Predictor
Student Performance Predictor
 
ML Lec 1 (1).pptx
ML Lec 1 (1).pptxML Lec 1 (1).pptx
ML Lec 1 (1).pptx
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 

More from Livares Technologies Pvt Ltd

Web Performance Optimization
Web Performance OptimizationWeb Performance Optimization
Web Performance Optimization
Livares Technologies Pvt Ltd
 
Software Architecture Design
Software Architecture DesignSoftware Architecture Design
Software Architecture Design
Livares Technologies Pvt Ltd
 
Automation using Appium
Automation using AppiumAutomation using Appium
Automation using Appium
Livares Technologies Pvt Ltd
 
Bubble(No code Tool)
Bubble(No code Tool)Bubble(No code Tool)
Bubble(No code Tool)
Livares Technologies Pvt Ltd
 
Unsupervised Machine Learning
Unsupervised Machine LearningUnsupervised Machine Learning
Unsupervised Machine Learning
Livares Technologies Pvt Ltd
 
Developing Secure Apps
Developing Secure AppsDeveloping Secure Apps
Developing Secure Apps
Livares Technologies Pvt Ltd
 
Micro-Frontend Architecture
Micro-Frontend ArchitectureMicro-Frontend Architecture
Micro-Frontend Architecture
Livares Technologies Pvt Ltd
 
Apache J meter
Apache J meterApache J meter
Apache J meter
Livares Technologies Pvt Ltd
 
Introduction to Angular JS
Introduction to Angular JSIntroduction to Angular JS
Introduction to Angular JS
Livares Technologies Pvt Ltd
 
An Insight into Quantum Computing
An Insight into Quantum ComputingAn Insight into Quantum Computing
An Insight into Quantum Computing
Livares Technologies Pvt Ltd
 
Just in Time (JIT)
Just in Time (JIT)Just in Time (JIT)
Just in Time (JIT)
Livares Technologies Pvt Ltd
 
Introduction to Bitcoin
Introduction to Bitcoin Introduction to Bitcoin
Introduction to Bitcoin
Livares Technologies Pvt Ltd
 
Data Mining Technniques
Data Mining TechnniquesData Mining Technniques
Data Mining Technniques
Livares Technologies Pvt Ltd
 
Facade Design Pattern
Facade Design PatternFacade Design Pattern
Facade Design Pattern
Livares Technologies Pvt Ltd
 
Manual Vs Automation Testing
Manual Vs Automation TestingManual Vs Automation Testing
Manual Vs Automation Testing
Livares Technologies Pvt Ltd
 
Screenless display
Screenless displayScreenless display
Screenless display
Livares Technologies Pvt Ltd
 
Database Overview
Database OverviewDatabase Overview
Database Overview
Livares Technologies Pvt Ltd
 
An Introduction to Machine Learning
An Introduction to Machine LearningAn Introduction to Machine Learning
An Introduction to Machine Learning
Livares Technologies Pvt Ltd
 
An Introduction to Face Detection
An Introduction to Face DetectionAn Introduction to Face Detection
An Introduction to Face Detection
Livares Technologies Pvt Ltd
 
Smart water meter solutions using LoRa WAN - Troncart
Smart water meter solutions using LoRa WAN - TroncartSmart water meter solutions using LoRa WAN - Troncart
Smart water meter solutions using LoRa WAN - Troncart
Livares Technologies Pvt Ltd
 

More from Livares Technologies Pvt Ltd (20)

Web Performance Optimization
Web Performance OptimizationWeb Performance Optimization
Web Performance Optimization
 
Software Architecture Design
Software Architecture DesignSoftware Architecture Design
Software Architecture Design
 
Automation using Appium
Automation using AppiumAutomation using Appium
Automation using Appium
 
Bubble(No code Tool)
Bubble(No code Tool)Bubble(No code Tool)
Bubble(No code Tool)
 
Unsupervised Machine Learning
Unsupervised Machine LearningUnsupervised Machine Learning
Unsupervised Machine Learning
 
Developing Secure Apps
Developing Secure AppsDeveloping Secure Apps
Developing Secure Apps
 
Micro-Frontend Architecture
Micro-Frontend ArchitectureMicro-Frontend Architecture
Micro-Frontend Architecture
 
Apache J meter
Apache J meterApache J meter
Apache J meter
 
Introduction to Angular JS
Introduction to Angular JSIntroduction to Angular JS
Introduction to Angular JS
 
An Insight into Quantum Computing
An Insight into Quantum ComputingAn Insight into Quantum Computing
An Insight into Quantum Computing
 
Just in Time (JIT)
Just in Time (JIT)Just in Time (JIT)
Just in Time (JIT)
 
Introduction to Bitcoin
Introduction to Bitcoin Introduction to Bitcoin
Introduction to Bitcoin
 
Data Mining Technniques
Data Mining TechnniquesData Mining Technniques
Data Mining Technniques
 
Facade Design Pattern
Facade Design PatternFacade Design Pattern
Facade Design Pattern
 
Manual Vs Automation Testing
Manual Vs Automation TestingManual Vs Automation Testing
Manual Vs Automation Testing
 
Screenless display
Screenless displayScreenless display
Screenless display
 
Database Overview
Database OverviewDatabase Overview
Database Overview
 
An Introduction to Machine Learning
An Introduction to Machine LearningAn Introduction to Machine Learning
An Introduction to Machine Learning
 
An Introduction to Face Detection
An Introduction to Face DetectionAn Introduction to Face Detection
An Introduction to Face Detection
 
Smart water meter solutions using LoRa WAN - Troncart
Smart water meter solutions using LoRa WAN - TroncartSmart water meter solutions using LoRa WAN - Troncart
Smart water meter solutions using LoRa WAN - Troncart
 

Recently uploaded

PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
ControlCase
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Zilliz
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Neo4j
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AIMind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Kumud Singh
 
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR EventsMonitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Ana-Maria Mihalceanu
 
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Aggregage
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Speck&Tech
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
Matthew Sinclair
 
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
名前 です男
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
DianaGray10
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems S.M.S.A.
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Matthew Sinclair
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Malak Abu Hammad
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
Claudio Di Ciccio
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
SOFTTECHHUB
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Paige Cruz
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 

Recently uploaded (20)

PCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase TeamPCI PIN Basics Webinar from the Controlcase Team
PCI PIN Basics Webinar from the Controlcase Team
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AIMind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
 
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR EventsMonitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
 
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
 
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 

Supervised Machine Learning

  • 1. SUPERVISED MACHINE LEARNING Presented By Amal Mohanan Livares Technologies Pvt Ltd Tech&Socio-Cultural Group
  • 2. Machine learning Continue … Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 3. Types of Machine Learning Continue… ⮚ Supervised Machine Learning ⮚ Unsupervised Machine Learning Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 4. What is Supervised Learning? Continue… Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 5. How Supervised Learning Works? Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 6. What is Unsupervised Learning? Continue… Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 7. Working of Unsupervised Learning Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 8. Steps Involved in Supervised Learning Continue… ⮚ Determine the type of training dataset ⮚ Collect/Gather the labelled training data. ⮚ Split the training dataset into training dataset, test dataset, ⮚ Determine the suitable algorithm for the model ⮚ Execute the algorithm on the training dataset. ⮚ Evaluate the accuracy of the model by providing the test set. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 9. Types of Supervised Learning Algorithms Continue… ⮚ Regression Analysis Regression analysis is a statistical method to model the relationship between a dependent (target) and independent (predictor) variables with one or more independent variables. ⮚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. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 10. Types Regression Analysis Continue… ⮚ Linear Regression Linear relationship between dependent and independent variables ⮚ Polynomial Regression Non linear relationship between dependent and independent variables Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 12. Linear Regression Continue… Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (x) variables, hence called as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 13. Assumptions of Linear Regression Continue… Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 14. Types of Linear Regression Analysis Continue… ▪ Simple Linear Regression ▪ Multiple Linear Regression Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 15. Simple Linear Regression Continue… Simple Linear Regression is a type of Regression algorithms that models the relationship between a dependent variable and a single independent variable. The relationship shown by a Simple Linear Regression model is linear or a sloped straight line, hence it is called Simple Linear Regression. Equation y = b0 + b1x b0 = It is the intercept of the Regression line b1 = It is the slope of the regression line, which tells whether the line is increasing or decreasing. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 16. Multiple Linear Regression Continue… Multiple Linear Regression is one of the important regression algorithms which models the linear relationship between a single dependent continuous variable and more than one independent variable. Equation y = b0 + b1x1 + b2x2 + b3x3 +….. b0,b1.b2,…. = Coefficients of the model x1, x2, x3, x4,...= Various Independent/feature variable Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 17. Backward Elimination Continue… Backward elimination is a feature selection technique while building a machine learning model. It is used to remove those features that do not have a significant effect on the dependent variable or prediction of output. There are various ways to build a model in Machine Learning, which are: ⮚ All-in ⮚ Backward Elimination ⮚ Forward Selection ⮚ Bidirectional Elimination ⮚ Score Comparison Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 18. Steps of Backward Elimination Continue… Step-1: Firstly, We need to select a significance level to stay in the model. (SL=0.05) Step-2: Fit the complete model with all possible predictors/independent variables Step-3: Choose the predictor which has the highest P-value, such that. 1. If P-value >SL, go to step 4. 2.Else Finish, and Our model is ready. Step-4: Remove that predictor. Step-5: Rebuild and fit the model with the remaining variables. Tech&Socio-Cultural Group Livares Technologies Pvt Ltd
  • 20. OUR CONTACT DETAILS Livares Technologies Pvt Ltd 5th Floor, Yamuna Building Technopark Phase III Campus Trivandrum, Kerala, India- 695581 Livares Technologies Pvt Ltd Tech&Socio-Cultural Group Our helpline is always open to receive any inquiry or feedback.Please feel free to contact us www.livares.com contact@livares.com @livaresofficial www.facebook.com/livaresofficial +91-471-2710003 | +91-471- 2710004 THANK YOU