machine learning.pptx
- 1. NOIDA INSTITUTE OF ENGINEERING AND TECHNOLOGY NAME –AAMIR KHAN SECTION – AIML-A ROLL NUMBER – 2101331530002 SUBMITTED TO– MRS. EKTA SINGH
- 3. WHAT IS MACHINE LEARNING? In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions.
- 4. HOW DOES MACHINE LEARNING WORK A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.
- 5. FEATURES OF MACHINE LEARNING: • Machine learning uses data to detect various patterns in a given dataset. • It can learn from past data and improve automatically. • It is a data-driven technology. • Machine learning is much similar to data mining as it also deals with the huge amount of the data.
- 6. NEED FOR MACHINE LEARNING The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us.
- 7. Classification of Machine Learning 1.Supervise d learning 1.Unsupervi sed learning 1.Reinforce ment learning
- 8. SUPERVISED LEARNING Supervised learning is a type of machine learning method in which we provide sample labelled data to the machine learning system in order to train it, and on that basis, it predicts the output.
- 9. UNSUPERVISED LEARNING Unsupervised learning is a learning method in which a machine learns without any supervision. The training is provided to the machine with the set of data that has not been labelled, classified, or categorized, and the algorithm needs to act on that data without any supervision. It can be further classifieds into two categories of algorithms: • Clustering • Association
- 10. REINFORCEMENT LEARNING Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance.