Machine Learning hi Artificial Intelligence Artificial Intelligence leh Data Science peng pakhat (subset), Algorithms hmanga thil nihphung hrang hrang chhut chhuak a, information tangkai taka chantir theihna a ni. AI hman tangkaina peng pawimawh tak a ni. Machine learning entirnan tha tak tak chu Google search engine, Twitter sentiment analysis, stock prediction, news classification, etc. te hi a ni.
Machine learning hi tunlai thil thar a ni.Industry hnathawh tam tak bakah kan nitin nunah a tangkai hle. Thil awmsa atanga an nih phung hrang hrang a lakhawm a, zir chhuah hi a tum ber a ni.
Internet kaltlanga sumdawng ten consumer duhzawng hriatthiam nan leh direct marketing emaw, customer hnena products advertisement design nan an hmang nasa hle. Spam filtering, fraud detection, smart healthcare system, speech recognition, computer vision, leh smart transportation te hi machine learning hmanna langsar tak an ni bawk.
Entirnan :Online shopping kan tih lai hian a kaihhnawih thil thenkhat kan lei tawh kan hmu a; chu zawng zawng chu Machine Learning rah chhuah a ni.
Shopping kan tih lai hian Products Bought Together tih section-ah product list kan hmu a, chu pawh chu Machine Learning entir nan a hmang bawk.
Machine learning hian video surveillance atanga smartphone chhui chhuah theihna thlengin a huam vek a ni. Machine learning hi internet search engine-ah te, email filter-ah te virus/spam thliar hran nan te, website-ah personalized recommendation siam nan te, banking software-ah te, kan phone-a apps tam tak, voice recognition ang chi ah leh thil danglam tak tak hmuhchhuahna atan hman a ni.
Social media platform Facebook ah te hian kan duhzawng, like leh post a zirin Machine learning hmangin advertisement an tarlang thin.e Chutiang bawkin Amazon ang shopping website-ah pawh algorithms hmangin customer-in thil lei leh a en dan a zirin thil lei tur min kawhhmuh thin.
Machine Learning Engineer Roles & Responsibilty
1. Data science leh data analytics prototype te zirchianna, siam danglam leh hman dan tur ruahman.
2. Machine learning atana hmanrualeh ruahmanna siam .
3. Test findings hmanga statistical analysis tih leh model siam that.
4. Internet hmanga training dataset awlsam taka hmuh theih tur zawn chhuah.
5. ML system leh model te hi a tul angin training leh retrained ni se.
6. ML framework leh library awm mekte tihchangtlun leh tihzauh nan.
7. Client emaw customer emaw mamawh ang zela machine learning application siam.
8. ML hmanrua leh algorithms dik tak chhui, test, leh practice-a hman.
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