Downloaded 106 times
More Related Content
Similar to Machine Learning Using Python
Machine Learning Using Python
- 1. JainAGMInstitute Of TechnologyJamkhandi 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 MachineLearning? 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 MachineLearning? 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 InputData Analyse Data Find Pattern Prediction/Decision Learns from the feedback
- 6. Types In MachineLearning The machine learns from the training data that is labelled No labelled training data Machine learns on it’s own
- 7. Types In MachineLearning 1.Supervised Machine Learning Machine Learning Model
- 8. Types In MachineLearning 2.Unsupervised Machine Learning Input Data Model Output
- 9. Types In MachineLearning 2.Reinforcemet Machine Learning
- 10. Types In MachineLearning
- 11.
- 12. K-Nearest neighbours KNearest 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 Considerthere 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 Linearregression 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 regressionline(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 Isit sunny? Go Swim Is it raining? Stay Indoor Walk the dog Yes Yes NO No
- 18. Naive Bayes The NaiveBayes 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
- 19.
- 20.
- 21.
- 31. Advantages 1. Automation ofEverything 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