Difference between Supervised and Unsupervised Machine Learning.pptx

Introduction to Machine Learning and Difference between Supervised and Unsupervised Machine Learning with examples.

1. 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.

2. Types of Machine Learning Continue… Supervised Machine Learning Unsupervised Machine Learning

3. 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.

4. How Supervised Learning Works? Continue…

5. 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.

6. Working of Unsupervised Learning Continue…

7. 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.

8. 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.

9. Types Regression Analysis Continue… Linear Regression Linear relationship between dependent and independent variables Polynomial Regression Non linear relationship between dependent and independent variables

10. Types Regression Analysis Continue…

11. 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

12. Assumptions of Linear Regression Continue…

13. Types of Linear Regression Analysis Continue… Simple Linear Regression Multiple Linear Regression

14. 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.

15. 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 x 1 , x 2 , x 3 , x 4 ,...= Various Independent/feature variable

16. Clustering Clustering is a technique for finding similarity groups in data, called clusters . it groups data instances that are similar to (near) each other in one cluster and data instances that are very different (far away) from each other into different clusters. 16

17. 17 An illustration The data set has three natural groups of data points, i.e., 3 natural clusters.

18. What is clustering for? Let us see some real-life examples: Example 1 : groups people of similar sizes together to make “small”, “medium” and “large” T-Shirts. Tailor-made for each person: too expensive One-size-fits-all: does not fit all. Example 2 : In marketing, segment customers according to their similarities To do targeted marketing.

19. What is clustering for? Example 3 : Given a collection of text documents, we want to organize them according to their content similarities. In fact, clustering is one of the most utilized data mining techniques. It has a long history, and used in almost every field, e.g., medicine , psychology, botany, sociology, biology, archeology , marketing, insurance, libraries, etc. In recent years, due to the rapid increase of online documents, text clustering becomes important.