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Machine Learning
- 1. Seminar on MachineLearning Submitted to: Prof. Manmindar Singh Submitted by: Rahul Kumar Gcs-1630043 Aquif Zubair Gcs-1630051 1
- 2. Agenda Introduction Basics Advantages Applications Classification Clustering Regression Use-Cases 2
- 3. Quick Questions…. How manypeople have heard about machine learning. How many people know about Machine learning.
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- 5. About Subfield ofArtificial Intelligence(AI) Name is derived from the concept that it deals with “construction and study of systems that can learn from data ” can be seen as building blocks to make computer learn to behave more intelligently.
- 6. The main advantageof ML Learning and writing an algorithm Its easy for human brain but it is tough for machine.it takes some time and good amount of training data for machine to accurately classify objects. Implementation and automation • This is easy for a Machine. Once learnt a machine can process one million images without any fatigue where as human brain can’t. • That’s why ML with big data is a deadly combination. 6
- 7. Applications of Machine Learning Banking / Telecom / Retail Identify: prospective customers Dissatisfied customers Good customers Bad payers Obtain: More effective advertising Less credit risk Fewer fraud 7
- 8. Applications of Machine Learning Biomedical / Biometrics Medicine: screening Drug discovery Security: Face recognition Signature / iris verification fingerprinting 8
- 9. Let’s dig deep intoit…. What do you mean by Apple
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- 11. Categories • Supervised Learning •Unsupervised Learning • Semi-Supervised Learning • Reinforcement Learning
- 12. Supervised Learning Thecorrect classes of the training data are known 12
- 13. Unsupervised Learning Thecorrect classes of the training data are not Known 13
- 14. Semi-Supervised Learning AMix of Supervised and Unsupervised learning 14
- 15. Reinforcement Learning Allowsthe machine or software agent to learn its behavior based on feedback from the environment. This behavior can be learnt once and for all, or keep adapting as time goes by 15
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- 17. Techniques Classification: predict class fromobservations Clustering: group observation into “meaningful” group Regression(presdiction):predi ct value from observations. 17
- 18. Classification Classify adocument into a predefined category. Documents can be text, images. The main goal of classification is to predict the target class(yes/no). Considering the student profile to predict whether the student will pass or fail. 18
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- 20. Clustering Clustering isthe task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other Objects are not predefined For e.g. these Keywords --”man’s shoe” --”Women’s shoe” --”women’s t-shirt” --”man’s t-shirt” --can be cluster into 2 categories “shoe” and “t-shirt” or “man” and “women” 20
- 21. Regression Is ameasure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables (e.g. time and cost) Regression analysis is a statistical process for estimating the relationship among variables. Regression means to predict the output value using training data. Popular one is Logistic regression (binary regression) 21
- 22. Classification vs Regression Classification Classification means to group the output into class. Classification to predict the type of humor i.e. harmful or not harmful using training data. If it is discrete/categorical variable ,then it is classification problem Regression Regression means to predict the output value using training data. Regression to predict the house price from training data. If it is real number/continuous then it is regression problem. 22
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- 24. Let’s see the usagesin real life Of machine learning
- 25. Use- cases SpamEmail Detection Machine Translation(Language Translation) Image Search(Similarity) Clustering(K Means):Amazon Classification : Google News Rating a Review Face Detection—Facebook’s photo tagging Fraud detection :Credit Card Providers 25
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