dynamic scenarios there are some scenarios which are dynamic in nature they keep changing over time in case of these scenarios and behaviorswe want machine to learn and take data-driven decisions some of the examples can be network connectivity and availability of infrastructure in an organization difficulty in translating expertise into computational tasks there can be various domains in which humans have their expertise,howeverthey are unable to translate this expertise into computational tasks in such circumstances we want machine learning the examples can be the domains of speech recognitioncognitive tasks etc machine learning model before discussing the machine learning modelwe must need to understand the following formal definition of ml given by professor mitchell" computer program is said to learn from experience with respect to some class of tasks and performance measure pif its performance at tasks in tas measured by pimproves with experience the above definition is basically focusing on three parametersalso the main components of any learning algorithmnamely task( )performance(pand experience (ein this contextwe can simplify this definition asml is field of ai consisting of learning algorithms thatimprove their performance (pat executing some task (tover time with experience (