What is Machine Learning? this is one of the populor niche technology in the data science , AI branch. There are a lot of question arise in you mind when you hear the term “Machine Learning”. Machine learning grew out of work in AI (Artificial Intelligence) and this is new capability of computers.
We can say that it’s extraction of knowledge of data.
1. Definitions of Machine Learning
Two definitions of Machine Learning are as:
“the field of study that gives computers the ability to learn without being explicitly programmed.”
– Arthur Samuel
Arthur Samuel definition is an older informal definition.
Tom Mitchell provides a more modern definition as follows:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
– Tom Mitchell
In another word we can say as,
Machine Learning study of algorithm that
– improve thier performance P
– at some task T
– with experience E
well defined learning task
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to below classifications:
2. Learning Type
- Supervised learning: Right answer given for each example of data
- Density estimation
3. Example of Machine Learning
- Supervised Learning – Classification: given mail is spam or not (is a type of binary classification )
- Supervised Learning – Regression: predicting stock market
- Unsupervised Learning – Clustering: market segmentation, Grouping of news, grouping of movie etc (with discrete data)
Your comments are welcome to improve this post of terminologies in machine learning. Happy learning 🙂