lack of sufficient human expertise in domain ( simulating navigations in unknown territories or even spatial planetsscenarios and behavior can keep changing over time ( availability of infrastructure in an organizationnetwork connectivityand so onhumans have sufficient expertise in the domain but it is extremely difficult to formally explain or translate this expertise into computational tasks ( speech recognitiontranslationscene recognitioncognitive tasksand so onaddressing domain specific problems at scale with huge volumes of data with too many complex conditions and constraints the previously mentioned scenarios are just several examples where making machines learn would be more effective than investing timeeffortand money in trying to build sub-par intelligent systems that might be limited in scopecoverageperformanceand intelligence we as humans and domain experts already have enough knowledge about the world and our respective domainswhich can be objectivesubjectiveand sometimes even intuitive with the availability of large volumes of historical datawe can leverage the machine learning paradigm to make machines perform specific tasks by gaining enough experience by observing patterns in data over period of time and then use this experience in solving tasks in the future with minimal manual intervention the core idea remains to make machines solve tasks that can be easily defined intuitively and almost involuntarily but extremely hard to define formally formal definition we are now ready to define machine learning formally you may have come across multiple definitions of machine learning by now which includetechniques to make machines intelligentautomation on steroidsautomating the task of automation itselfthe sexiest job of the st centurymaking computers learn by themselves and countless otherswhile all of them are good quotes and true to certain extentsthe best way to define machine learning would be to start from the basics of machine learning as defined by renowned professor tom mitchell in the idea of machine learning is that there will be some learning algorithm that will help the machine learn from data professor mitchell defined it as follows " 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 while this definition might seem daunting at firsti ask you go read through it couple of times slowly focusing on the three parameters--tpand --which are the main components of any learning algorithmas depicted in figure -