Machine Learning(ML) at a Glance
Introduction of Machine Learning(ML)
Machine learning is a subset of artificial intelligence. ML goal is to design a machine or a computer program to use past data, and experience to solve a given problem using simple statistical methods, algorithms, and modern computing power.
ML is the “field of study that gives computers the ability to learn without being explicitly programmed.” -By Arthur Samuel
In Nutshell we can say that “ML is about making predictions with the help of past experience and data”.
According to Tom Mitchell (in 1997) “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Classification of ML
- Supervise Learning
Supervised learning occurs when a machine or program learns from example data and associated target responses that can consist of numeric values. Supervised Learning is really fast and accurate. Supervised learning fall into three categories: Classification, Regression and Forecasting.
- Unsupervised Learning
In Unsupervised ML, we do not have labeled data and outcome variables to predict. Under the umbrella of unsupervised learning, fall -: Clustering and Dimension reduction.
- Reinforcement Learning
Reinforcement learning teaches the machine trial and error. Reinforcement learning the agent (Algorithm) continuously learns from the environment in an iterative fashion.