Machine Learning is a new field which we call inductive learning. Inductive learning is designed to bring new rules and predict future activities. Inductive learning is a learning that is derived from observation and knowledge (rules and conclusions). In other words, “inductive learning is a process of learning through examples.
Statistics:-
Statistics is a tool for data mining and it is a field of mathematics in which the data is collected, analyzed, interpreted, organized and presented.
Machine Learning has following definition:-
Machine learning is a field of computer science that gives computers a capability to learn without programming.
Or
Machine learning is a process in which computers learn from data, act and predict like humans and improve the ability to learn over time.
Or
the machine is a group of learning tools, from which the computer learns how the task is performed.
Machine learning occurs when the machine itself learns something from itself like: – playing chess, OCR (optical cracker recognition) and other things. Machine Learning uses algorithms to learn things like: – Cluster, Neural Networks and Other Algorithms.
Machine Learning allows computers to learn new things through analysis, self-training, observation, experience which can handle new situations.
For example:-
The best example of machine learning is- facebook, if you read or like post or image of any friend in Facebook, then the news feed will show you the more content related to that particular friend and his or her friends.
Types of Machine Learning:-
The following is the type of machine learning-
1. Supervised learning
2. Unsupervised learning
Supervised learning: –In this learning, various types of labeled examples and answers are given as input from which the algorithm learn from these examples which further predict the correct result based on these inputs.
Example- A spam filter in the email, i.e. spam message in the email, so that spam messages are moved to the spam folder.
Two common types of supervised learning- Classification and Regression
Unsupervised learning: –This is a little complicated because the correct answer and label are not given in it. The algorithm through which it analyzes pattern is already present in the data.
Example- Google News
Two common types of unsupervised learning– Dimension reduction and Clustering.
