ML approaches are based on a set of statistical and mathematical algorithms carrying out tasks such as classification, regression analysis, concept learning, predictive modeling, clustering, and mining of useful patterns. Using ML, we aim to improve the whole learning process automatically so that we may not need complete human interactions, or so that we can at least reduce the level of such interactions as much as possible.
A soft introduction to ML
Working principle of a learning algorithm
Tom M. Mitchell explained what learning really means from a computer science perspective:
"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...
