machine learning basics the idea of making intelligentsentientand self-aware machines is not something that suddenly came into existence in the last few years in fact lot of lore from greek mythology talks about intelligent machines and inventions having self-awareness and intelligence of their own the origins and the evolution of the computer have been really revolutionary over period of several centuriesstarting from the basic abacus and its descendant the slide rule in the th century to the first general purpose computer designed by charles babbage in the in factonce computers started evolving with the invention of the analytical engine by babbage and the first computer programwhich was written by ada lovelace in people started wondering and contemplating that could there be time when computers or machines truly become intelligent and start thinking for themselves in factthe renowned computer scientistalan turingwas highly influential in the development of theoretical computer sciencealgorithmsand formal language and addressed concepts like artificial intelligence and machine learning as early as the this brief insight into the evolution of making machines learn is just to give you an idea of something that has been out there since centuries but has recently started gaining lot of attention and focus with faster computersbetter processingbetter computation powerand more storagewe have been living in what like to callthe "age of informationor the "age of dataday in and day outwe deal with managing big data and building intelligent systems by using concepts and methodologies from data scienceartificial intelligencedata miningand machine learning of coursemost of you must have heard many of the terms just mentioned and come across sayings like "data is the new oilthe main challenge that businesses and organizations have embarked on in the last decade is to use approaches to try to make sense of all the data that they have and use valuable information and insights from it in order to make better decisions indeed with great advancements in technologyincluding availability of cheap and massive computinghardware (including gpusand storagewe have seen thriving ecosystem built around domains like artificial intelligencemachine learningand most recently deep learning researchersdevelopersdata scientistsand engineers are working continuously round the clock to research and build toolsframeworksalgorithmstechniquesand methodologies to build intelligent models and systems that can predict eventsautomate tasksperform complex analysesdetect anomaliesself-heal failuresand even understand and respond to human inputs this follows structured approach to cover various conceptsmethodologiesand ideas associated with machine learning the core idea is to give you enough background on why we need machine learningthe fundamental building blocks of machine learningand what machine learning offers us presently this will enable you to learn about how best you can leverage machine learning to get the maximum from your data since this is book on practical machine learningwhile we will be focused on specific use casesproblemsand real-world case studies in subsequent it is extremely important to understand formal definitionsconceptsand foundations with regard to learning algorithmsdata managementmodel buildingevaluationand deployment hencewe cover all these aspectsincluding industry standards related to data mining and machine learning workflowsso that it gives you foundational framework that can be applied to approach and tackle any of the real-world problems we solve (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python