1) What is Machine learning?
Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.
2) Mention the difference between Data Mining and Machine learning?
Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used
3) What is ‘Overfitting’ in Machine learning?
In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.
4) What is inductive machine learning?
The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.
5)What are the five popular algorithms of Machine Learning?
*Decision Trees
*Neural Networks (back propagation)
*Probabilistic networks
*Nearest Neighbor
*Support vector machines