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Machine learning Presentation
- 1. MATS University Mats University MATS School of Information Technology Presentation ON MACHINE LEARNING NAME: MANISH SINGH ROLL NO: MU19MCA(L)007
- 2. CONTENTS Why Machine learning? Defining Machine Learning Traditional Programming vs Machine Learning Application of Machine Learning Process of Learning Machine Learning Algorithm Decision Learning Machine Learning scope Limitation Of Machine Learning? Software Conclusion References
- 3. Why Machine Learning? Develop systems that can automatically adapt and customize themselves to individual users. Discover new knowledge from large databases (data mining). Ability to mimic human and replace certain monotonous tasks. Develop systems that are too difficult/expensive to construct manually.
- 4. Defining Machine Learning. Machine learning is a method of data analysis that automates analytical model building. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. Machine learning algorithms build mathematical model based on sample data, known as "training data“. Machine learning is closely related to computational statistics. Python language suitable for a variety of tasks in machine learning.
- 5. Traditional Programming vs Machine Learning Traditional Programming Data Output Program Machine Learning Data Program Program Computer Computer
- 6. Application of Machine Learning
- 7. Process of Learning Gathering Data Data preparation Choosing a model Training Evaluation Parameter Tuning Prediction
- 8. Contd…
- 9. Contd… Learning = Improving with experience at some task Improve over task T, With respect to performance measure, P Based on experience, E
- 10. Machine Learning Algorithm Supervised Learning Regressions: learning numbers Classifications: learning classes Unsupervised Learning Clustering: finding groups Dimensionality Reductions: finding efficient representations Semi-supervised Learning Reinforcement Learning
- 11. Decision Learning Decision tree learning is one of the predictive modeling approaches used in machine learning. A decision tree can be used to visually and explicitly represent decisions and decision making. It uses a decision tree to go from observations about an item to conclusions about the item's target value. A decision tree is drawn upside down with its root at the top.
- 12. Contd… In the image on the left, the bold text in black represents a condition/internal node, based on which the tree splits into branches/ edges. The end of the branch that doesn’t split anymore is the decision/leaf, in this case, whether the passenger died or survived, represented as red and green text respectively.
- 13. Machine Learning Scope Machine Learning in Search Engine Defining Machine Learning Traditional Programming vs Machine Learning Application of Machine Learning Process of Learning
- 14. Limitation Of Machine Learning Accuracy depends on training learning which is not always available. Have large Data sets requirements to learn about various topics which may be time taken and require various resources. A Machine cannot learn if there is no data. Performance Machine Learning cannot be Guaranteed.
- 15. Software Suites Python scikit learn MATLAB, mlpy Oracle data mining STATISTICA data miner Orange R langauge
- 16. Conclusion Machine learning is quickly growing field in computer science. It has applications in nearly every other field of study. It is already being implemented commercially because machine learning can solve problems too difficult or time consuming for humans to solve. To describe machine learning in general terms, a variety models are used to learn patterns in data and make accurate predictions based on the patterns it observes.
- 17. References View Of People -VOP (viewofpeoples.xyz)
- 18. THANK YOU