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Machine Learning Using Python

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SavitaHanchinal

This document discusses machine learning and provides examples of common machine learning algorithms. It begins with definitions of machine learning and the machine learning process. It then describes four main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and discusses five common algorithms - K-nearest neighbors, linear regression, decision trees, naive Bayes, and support vector machines. It concludes with an overview of a heart disease prediction mini-project using Python.

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JainAGMInstitute Of Technology Jamkhandi Machine Learning A internship submitted in partial fulfilment of the requirements for the degree of BACHELOR OF TECHNOLOGY in ELECTRONICS AND COMMUNICATION ENGINEERING Savita L Hanchinal USN-2JG17EC004 Department of Electronics and Communication Engineering Under the guidance of Mr. Allayya Kudli Assistant Professor
Machine Learning Using Python
1.What Is Machine Learning? Overview 2.Machine Learning Process 3.Types Of Machine Learning 4.Machine Learning Algorithms 5.Mini Project(Heart Disease Prediction) Using Python 5 4 3 2 1
What is Machine Learning? Machine Learning is an application of Artificial Intelligence (AI) that provides system the ability to automatically learn and improve from experience without being explicitly programmed Ordinary System With Artificial Intelligence Machine Learning Predicts Improves
Machine Learning Process Input Data Analyse Data Find Pattern Prediction/Decision Learns from the feedback
Types In Machine Learning The machine learns from the training data that is labelled No labelled training data Machine learns on it’s own
Types In Machine Learning 1.Supervised Machine Learning Machine Learning Model
Types In Machine Learning 2.Unsupervised Machine Learning Input Data Model Output
Types In Machine Learning 2.Reinforcemet Machine Learning
Types In Machine Learning
Machine Learning Algorithm
K-Nearest neighbours  K Nearest Neighbour is a simple algorithm that stores all the available cases and classifies the new data or case based on a similarity measure. It is mostly used to classifies a data point based on how its neighbours are classified.  In K Nearest neighbour K can be a integer greater than 1.So ,far every new data point we want to classify, we compute to which neighbouring group it is closest.
K-Nearest neighbours Example cost Durability Consider there are three clusters Football Basket ball Tennis ball Now we have anew data point (blackball) We try to classify using KNN Here lets us take K=8 Here the majority of balls within circle are tennis ball So the unknown ball is classified as tennis ball Draw a circle
Linear Regression  Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
Linear Regression The regression line(green line) has the least value of D D=d12+d22+d32+d42+d52+d62+d72+d82+d92 d1 d2 d3 d4 d5 d6 d8 d7 d9 X Y
Decision Tree Decision Tree : Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.
Decision Tree Example Is it sunny? Go Swim Is it raining? Stay Indoor Walk the dog Yes Yes NO No
Naive Bayes The Naive Bayes Classifier technique is based on conditional probability and is practically suited when the complexity of inputs is high Receives Mail Old spam mail records Applying Bayes Theorem Predicts weather a mail is spam
Naive Bayes Example
Support Vector Machine(SVM) X Y Hyperplane Marginal Plane
Heart Disease Prediction Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Machine Learning Using Python
Advantages 1. Automation of Everything 2. Wide Range of Applications 3. Scope of Improvement 4. Efficient Handling of Data 5. Best for Education and Online Shopping Disadvantages 1. Possibility of High Error 2. Algorithm Selection 3. Data Acquisition 4. Time and Space
Applications 1. Image Recognisation 2. Speech Recognisation 3. Product Recomandation 4. Virtual Personal assistance 5. Medical diagnosis 6. Automatic language translation
Machine Learning Using Python

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Machine Learning Using Python

  • 1. JainAGMInstitute Of Technology Jamkhandi Machine Learning A internship submitted in partial fulfilment of the requirements for the degree of BACHELOR OF TECHNOLOGY in ELECTRONICS AND COMMUNICATION ENGINEERING Savita L Hanchinal USN-2JG17EC004 Department of Electronics and Communication Engineering Under the guidance of Mr. Allayya Kudli Assistant Professor
  • 3. 1.What Is Machine Learning? Overview 2.Machine Learning Process 3.Types Of Machine Learning 4.Machine Learning Algorithms 5.Mini Project(Heart Disease Prediction) Using Python 5 4 3 2 1
  • 4. What is Machine Learning? Machine Learning is an application of Artificial Intelligence (AI) that provides system the ability to automatically learn and improve from experience without being explicitly programmed Ordinary System With Artificial Intelligence Machine Learning Predicts Improves
  • 5. Machine Learning Process Input Data Analyse Data Find Pattern Prediction/Decision Learns from the feedback
  • 6. Types In Machine Learning The machine learns from the training data that is labelled No labelled training data Machine learns on it’s own
  • 7. Types In Machine Learning 1.Supervised Machine Learning Machine Learning Model
  • 8. Types In Machine Learning 2.Unsupervised Machine Learning Input Data Model Output
  • 9. Types In Machine Learning 2.Reinforcemet Machine Learning
  • 10. Types In Machine Learning
  • 12. K-Nearest neighbours  K Nearest Neighbour is a simple algorithm that stores all the available cases and classifies the new data or case based on a similarity measure. It is mostly used to classifies a data point based on how its neighbours are classified.  In K Nearest neighbour K can be a integer greater than 1.So ,far every new data point we want to classify, we compute to which neighbouring group it is closest.
  • 13. K-Nearest neighbours Example cost Durability Consider there are three clusters Football Basket ball Tennis ball Now we have anew data point (blackball) We try to classify using KNN Here lets us take K=8 Here the majority of balls within circle are tennis ball So the unknown ball is classified as tennis ball Draw a circle
  • 14. Linear Regression  Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
  • 15. Linear Regression The regression line(green line) has the least value of D D=d12+d22+d32+d42+d52+d62+d72+d82+d92 d1 d2 d3 d4 d5 d6 d8 d7 d9 X Y
  • 16. Decision Tree Decision Tree : Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.
  • 17. Decision Tree Example Is it sunny? Go Swim Is it raining? Stay Indoor Walk the dog Yes Yes NO No
  • 18. Naive Bayes The Naive Bayes Classifier technique is based on conditional probability and is practically suited when the complexity of inputs is high Receives Mail Old spam mail records Applying Bayes Theorem Predicts weather a mail is spam
  • 31. Advantages 1. Automation of Everything 2. Wide Range of Applications 3. Scope of Improvement 4. Efficient Handling of Data 5. Best for Education and Online Shopping Disadvantages 1. Possibility of High Error 2. Algorithm Selection 3. Data Acquisition 4. Time and Space
  • 32. Applications 1. Image Recognisation 2. Speech Recognisation 3. Product Recomandation 4. Virtual Personal assistance 5. Medical diagnosis 6. Automatic language translation