Waa maxay Machine Learning (ML) Muxuuse kaga duwan yahay Artificial intellegence (AI)?
Table of Contents
Hordhac
Dunidan technology-ga casriga ah la isticmaalayo, ereyada Machine Learning (ML) iyo Artificial Intelligence (AI) waa kuwo badana abuura ismaandhaaf iyo in la isku qaldo. Hadaba waa maxay Machine Learning (ML) siduuse uga duwanyahay AI ? Maqaalkani wuxuu gudaha u galayaa qaabdhismeedka ML, waxaynu sidoo kale isla eegi doonaa xiriirka uu la leeyahay AI, wuxuu maqaalkani sidoo kale qeexayaa sababta ay udub dhexaadka ugu tahay in lala jaan qaado technology-ga casriga ah ee maanta aduunka ka jira.
Fahamka Machine Learning iyo wuxuu kaga duwanyahay AI
Machine Learning waa qayb kamida AI, isla markaana shaqadeedu tahay sidii ay ugu tababari lahayd computerska ama machines-ka inay la falgalaan waxna ka barankaraan Data-da iyagoo markaas wixii ay barteen la qabsanaya oo waliba gaadhaya go’aano ku salaysan wixii ay ka barteen Data-daas. Halka AI uu yahay mawduuc balaadhan oo science-ka ka mid ah isla markaana isku dayaya in uu sameeyo wax u eg waxa bini’aadamku samaynayo, ML waxaa isaga shaqadiisu tahay keliya xisaabaadka algorithmska si uu ugu ogolaado computer-ka in la falgalo Data isla markaana go’aamo isaga u gaara uu la yimaado
Qaab-dhismeedka Machine Learning
Machine Learning wuxuu ushaqeeyaa 3 qaab oo waawayn, oo lakala dhaho, supervised, unsupervised, and reinforcement learning. Nooc kastaa wuxuu u adeegaa ujeedooyin kala duwan laakiin asal ahaan wuxuu waxay ka simanyihiin fikradda hagaajinta go’aan qaadashada xogta.
- Supervised Learning: Machine-ka waxaa marka hore lagu tababarayaa xog loogu talagalay inuu ku qaato tababarkiisa, si uu ubarto una saadaaliyo figrado imaankara oo aan qofka caadiga ah kadhex arkayn Data-da uu markaas haysto. Qaybtan xogta kuu diyaarka (Data) waxaad 70-80% ku siinaysaa machine-ka tababar ahaan, halka 30-20% xogta soo hadhayna aad markaas ku tijaabinayso tababarkii la siisay sida uu u qaatay iyo natiijada uu keeni doono. Xusuusnow qaybtan markasta xog aad hayso oo kuu diyaar ah ayaad isticmaalaysaa.
- Unsupervised Learning: Qaybta Shaqada guud waa iskaga mid Supervised Learning, lakin qaybtan wax tababar ah oo la siinayo majirto, wuxuu la falagalayaa ama isku dayayaa inuu xog ka keeno Data aadan adigu siin, ama ogayn meel uu ka keenay.
- Reinforcement Learning: Qaybtan hadaynu si fudud usharaxno, kaba soo qaad cunugaaga oo 3 jir ah oo aad ku tababarayso inuu dharka isagu iska bixiyo, gashado, sidoo kale cuntada gacantiisa ku cuno. Hadii uu tababarkaas kasoo baxo waxad ubalan qaaday abaal marin, haduu kasoo bixi waayona malahan wax abaalmarin ah. Cunugaas wuxuu ku dadaalayaa inuu sifiican tababarka u qaato si uu abaalmarin u helo. Sidaas oo kale booska cunuga waxa ku jira “Agent” oo ah computer kaas oo barta in uu go’aan ka gaadho samaynta waxqabadyada environment-ga. Environment wuxuu bixiyaa jawaab celin qaab abaal-marin ama ganaax oo kale ah. Tusaale ahaan waxa qaybtan ku shaqeeya video gaming-ka.
Marxaladaha uu maro Machine Learning
Bilaabidda mashruuca ML waxa jira oo ay martaa dhowr marxaladood oo muhiim ah, laga soo bilaabo xog ururinta ilaa qaybta u danbaysa oo ah la socoshada iyo wax kebedista mashruucaas:
- Data Collection: Soo ururinta xog tayo sare leh, taasoo xiriir la leh problemka markaas la rabo in laxaliyo.
- Data Preprocessing: sidoo kale waxa loo yaqaan Data Clining, waa nadiifinta iyo qaabaynta xogta si loo waafajiyo processka lagu shaqayndoono.
- Feature Engineering: Doorashada ama abuurista features cusub oo laga soo saaro xogta si loo horumariyo waxqabadka moodeelka (Model=Michine).
- Model Selection: Doorashada algorithmiga ku haboon ee ML, kaasoo ku saleysan nooca dhibaatada markaas la rabo in la xaliyo.
- Training: In la tababaro markaas machine-ka si uu ubarto Xogta loo diyaariyay waxay tahay iyo siday ushaqaynayso.
- Evaluation: In la qiimeeyo markaas shaqada uu qabtay iyo jawabta uu ka keenay xogtii tababar ahaanta loo siiyay.
- Deployment: Markan waa marka aad Machine-kaaga (Modelka) ka dhigayso mid toos ushaqo gala, kana gudbay tababarkii.
- Monitoring and Updating: In si joogto ah u loola socdo waxna looga bedelo hadii loo baahdo
Saamaynta ML ee Dunida Maanta
ML waxay noqotay lama huraan qaybo kala duwan oo nolosha ah, laga bilaabo daryeelka caafimaadka, halkaas oo ay sidegdeg ah usoo bandhigto baaritaanno sax ah. Sidoo kale dhanka madadaalada, waxaa jira shirkado ay kamid tahay Netflix oo u isticmaala in ML algorithm uu qofka filmka daawanaya uu isla markaas kuusoo bandhigo filmkii hore kuwo lamid ah ama ka ag dhow. Wali ma istidhi waxyaabo aad aad rabtay oo internetka xayaysiis ahaan kaagasoo horbaxaya, yaa usheegay inaan waxan rabo ? waa ML.. Awoodda ML ee habaynta iyo falanqaynta xaddi aad u badan oo xog ah ayaa bedeshay hababka go’aan samaynta, taas oo ka dhigaysa inay soo gudbiyaan xog badan oo sax ah.
Sahaminta Machine Learning Algorithms
Waxaa jira algorithms badan oo ML ah, mid kastaa wuxuu ku habboon yahay hawlo kala duwan. Laga soo bilaabo linear regression to convolutional neural networks (CNNs) oo ah algorithmka loo isticmaalo in machine-ku aqoonsado sawirada. Algorithms-ga ML waa mid aad ballaaran oo kala duduwan. Fahamka aasaaska algorithms-yadanan ayaa muhiim u ah qof kasta oo raadinaya inuu u hayaamo barashada ML.
Doorka ML ee AI Models-ka: Aan isla eegno ChatGPT
ChatGPT, language model ay soo saartay OpenAI, ayaa tusaale u ah sida ML looga faa’iidaysan karo casriga AI-ga. Waxay adeegsadaa deep learning oo ah qayb-hoosaad kamida ML-ka, si uu u fahmo oo uu u curiyo qoraal u eg mid bini’aadam qoray, jawaabta uu ku siinayona mar walba waa mid ku xiran qaabka aad wax u waydiisay. ChatGPT iyo modelska la midka ah waxay muujinayaan awoodaha ML iyo siday ula falgelayaan waxa loo yaqaan natural languages, iyagoo muujinaya waxtarkooda abuuritaan ee mawduucyada kala duwan sida qoraal, muuqaal, kala saaris codadka iwm. Halkan ka fiiri AI-toolska ay samayso 5a-hub anagoo isticmaalayna custom chatGPT, riix halkan.
Tusaale Practical ML : Email Classification in Jupyter Notebook
Aan isla eegno sawirada tusaale yar oo aanu ku samaynayo ML algorithmka la dhaho Naive Bayes, si aynu ukala saarno emailka inoo yimaada inuu yahay spam iyo inkale. Code-kan waxan kusoo bandhigaynaa emails diyaarsan oo la isku dhafay, isaga oo u kala saaraya ‘spam’ ama ‘non-spam’ . Kadib waxaynu ku tababaraynaa modelka Naive Bayes xogtan, inagoo qiimeynayna awooda uu u leedahay inuu si sax ah u kala saarto iimaylada cusub. Aragtida sawirka, waxaan ku arki karnaa saadaasha moodeelka marka la barbar dhigo calaamadaha dhabta ah, iyagoo siinaya aragti cad oo ku saabsan waxqabadkiisa.
Tusaalahan, waxaan isticmaaleynaa program-ka Jupyter Notebook, oo ah aalad awood badan oo noo ogolaaneysa inaan si is dhexgal ah u qorno oo u socodsiino code Python ah inagoo isticmaalayna librarys kala duwan. Programkan waxad ku samaynkartaa dhamaan stageska loogu talagalay Data science ama Data analysing.
Dhamaan
Machine Learning is not just a component of AI; it’s a foundational technology that enables machines to make sense of data, learn from it, and make decisions. Its application across different domains has shown that ML can enhance efficiency, improve accuracy, and open new possibilities. Whether you’re a seasoned tech professional or a curious beginner, understanding ML is key to navigating the future of technology.
Machine Learning maaha kaliya qayb ka mid ah AI; waa tignoolajiyada aasaaska u ah in mashiinada awood u yeeshaan inay xog micno leh soo saaraan, wax ka bartaan xogta la siiyay, oo ay go’aano qaataan. Programska ku shaqeeya ML oo loo isticmaala meelo kala duwan, ayaa ah kuwo la arkay hufnaantooda waxqabad, fududaynta suurtagalnimada in lahelo xog sax ah, sidoo kale ay soo saareen fursado cusub oo aan horey loo haynin. Haddi aad tahay xirfadle dhanka teknoolojiga oo khibrad leh ama qof ku cusub ML, fahamka technology-gan iyo siduu u shaqeeyo ayaa fure u ah guulo badan oo aad ka gaadhikarta ML waqtigan lagu jiro.
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