Time: 2025-07-11 11:46:28 Source: Codora.ai Author: c Reading: 733 times

20201.7 MB of data will be ai tutorial for beginnerscreated everysecond for every person on Earth so asI'm speaking to you right now I'mgenerating a lot of data now youwatching this video on YouTube alsoaccounts for data generation so there'sdata everywhere so with the availabilityof so much data it is finally possibleto build predictive models that canstudy and analyze complex data to finduseful insights and deliver moreaccurate results so Top tire companieslike Netflix and Amazon build suchmachine learning models by using tons ofdata in order to identify any profitableopportunity and avoid any unwanted riskso guys one thing you all need to knowis that the most important thing forartificial intelligence is data forartificial intelligence or whether it'smachine learning or deep learning it'salways data and now that we have a lotof data we can find a way to analyzeprocess and draw useful insights fromthis data in order to help us growbusinesses or to find solutions to someproblems data is the solution we justneed to know how to handle the data andthe way to handle data is throughmachine learning deep learning andartificial intelligence a few reasonswhy machine learning is so important isnumber one due to increase in datageneration so due to excessiveproduction of data we need to find amethod that can be used to structureanalyze and draw use useful insightsfrom data this is why machine learningcomes in right it is used to solveproblems and find solutions to the mostcomplex tasks faced by organizationsapart from this we also needed toimprove decision making so by making useof various algorithms machine learningcan be used to make Better Businessdecisions for example machine learningis used to forecast sales it is used topredict any downfalls in the stockmarket or identify any sort of risk andanomalies other reasons include thatmachine learning helps us uncoverpatterns and Trends in data so findinghidden patterns and extracting keyinsights from data is the most importantpart of machine learning so by buildingpredictive models and using statisticaltechniques machine learning allows youto dig beneath the surface and explorethe data at a minute scale understandingdata and extracting patterns manuallytakes a lot of time right it takesseveral days for us to EXT any usefulinformation from data but if you usemachine learning algorithms you canperform similar computations in lessthan a second another reason is we needto solve complex problems so fromdetecting the genes linked to the deadlyALS disease to building self-drivingcars machine learning can be used tosolve the most to most complex problemsat present we've also found a way tospot Stars which are like 2,400 lightyears away from our planet okay all ofthis is possible through AI machinelearning deep learning and thesetechniques so to Summit up machinelearning is very important at presentbecause we're facing a lot of issueswith data we're generating a lot of dataand we have to handle this data in sucha way that it benefits us so that's whymachine learning comes in moving on whatexactly is machine learning so let megive you a short history of machinelearning so machine learning was firstcoined by Arthur Samuel in the year 1959which is just 3 years from whenartificial intelligence was coined rightso looking back that year was probablythe most significant in terms oftechnological advancements because mostof the Technologies today are based onthe concept of machine learning most ofthe AI Technologies itself are based onthe concept of machine learning and deeplearning don't get confused aboutmachine learning and deep learning we'lldiscuss about deep learning in thefurther slides where we'll also see thedifference between AI machine learningand deep learning so coming back to whatexactly machine learning is so if youbrowse to the internet you'll find a lotof definitions about what exactlymachine learning is one of thedefinitions I found was a computerprogram is set to learn from experiencee with respect to some class of task Tand performance measure P if itsperformance at tasks in t as measured byP improves with experience e that's veryconfusing so let me just narrow it downto you in simple terms machine learningis a subset of artificial intelligencewhich provides machines the ability tolearn automatically and improve fromexperience without being explicitlyprogrammed to do so in the sense it isthe practice of getting machines tosolve problems by gaining the ability tothink but now you might be thinking howcan a machine think or make decisionsnow machines are very similar to humansokay if you feed a machine a good amountof data it will learn how to interpretprocess and analyze this data by usingmachine learning algorithms and it willhelp you solve real world problems sowhat happens here is a lot of data isfed to the machine the machine willtrain on this data and it'll build apredictive model with the help ofmachine learning algorithms in order topredict some outcome or in order to findsome solution to a problem so itinvolves data you're going to train themachine and build a model by usingmachine learning algorithm in order topredict some outcome or to find asolution to a problem so that is asimple way of understanding what exactlymachine learning is I'll be going intomore depth about machine learning sodon't worry if you haven't understoodanything as of now now let's discuss acouple of terms which are frequentlyused in machine learning right so thefirst definition that we come acrossvery often is an algorithm all right sobasically a machine learning algorithmis a set of rules and statisticaltechniques that is used to learnpatterns from data and draw significantinformation from it okay so guys thelogic behind a machine learning model isbasically the machine learning algorithmokay an example of a machine learningalgorithm is linear regression ordecision tree or a random forest all ofthese are machine learning algorithmswhich Define the logic behind a machinelearning model now what is a machinelearning model a model is actually themain component of a machine learningprocess okay so a model is trained byusing the machine learning algorithm nowthe difference between an algorithm anda model is that an algorithm maps allthe decisions that a model is supposedto take based on the given input inorder to get the correct output allright so the model will use the machinelearning algorithm in order to drawuseful insights from the input and

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