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# Sissejuhatus masinõppesse
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ml/)

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# Alustamine
## Juhised

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# Masinõppe ajalugu
![Masinõppe ajaloo kokkuvõte visandmärkmetes](../../../../translated_images/et/ml-history.a1bdfd4ce1f464d9.webp)

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# Loo ajajoon
## Juhised

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# Masinõppe lahenduste loomine vastutustundliku tehisintellektiga
![Vastutustundliku tehisintellekti kokkuvõte masinõppes sketšimärkmetes](../../../../translated_images/et/ml-fairness.ef296ebec6afc98a.webp)

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# Uurige vastutustundliku tehisintellekti tööriistakomplekti
## Juhised

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# Masinõppe tehnikad
Masinõppe mudelite ja nende kasutatava andmete loomise, kasutamise ja hooldamise protsess erineb oluliselt paljudest teistest arendusvoogudest. Selles õppetükis selgitame seda protsessi ja toome välja peamised tehnikad, mida peate teadma. Te saate:

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# Intervjueeri andmeteadlast
## Juhised

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# Sissejuhatus masinõppesse
Selles õppekava osas tutvustatakse teile masinõppe valdkonna aluskontseptsioone, mis see on, ning saate teada selle ajaloost ja tehnikatest, mida teadlased selle valdkonnaga töötamiseks kasutavad. Uurime koos seda uut ML-i maailma!

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# Alusta Pythoni ja Scikit-learniga regressioonimudelite jaoks
![Regressioonide kokkuvõte visuaalses märkmes](../../../../translated_images/et/ml-regression.4e4f70e3b3ed446e.webp)

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# Regressioon Scikit-learniga
## Juhised

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See on ajutine kohatäide
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# Ehita regressioonimudel Scikit-learniga: andmete ettevalmistamine ja visualiseerimine
![Andmete visualiseerimise infograafika](../../../../translated_images/et/data-visualization.54e56dded7c1a804.webp)

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# Visualisatsioonide uurimine
Andmete visualiseerimiseks on saadaval mitmeid erinevaid teeke. Looge selle õppetunni kõrvitsate andmete põhjal mõned visualisatsioonid, kasutades matplotlibi ja seaborni näidisnotebookis. Milliste teekidega on lihtsam töötada?

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See on ajutine kohatäide
---

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# Ehita regressioonimudel Scikit-learniga: neli viisi regressiooniks
![Lineaarse ja polünoomse regressiooni infograafika](../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.webp)

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# Loo regressioonimudel
## Juhised

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See on ajutine kohatäide
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# Logistiline regressioon kategooriate ennustamiseks
![Logistilise ja lineaarse regressiooni infograafik](../../../../translated_images/et/linear-vs-logistic.ba180bf95e7ee667.webp)

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# Mõne regressiooni uuesti proovimine
## Juhised

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See on ajutine kohatäide
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# Regressioonimudelid masinõppes
## Piirkondlik teema: Kõrvitsahindade regressioonimudelid Põhja-Ameerikas 🎃

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# Ehita veebirakendus ML-mudeli kasutamiseks
Selles tunnis treenid ML-mudelit andmekogumiga, mis on täiesti teistsugune: _UFO-vaatlused viimase sajandi jooksul_, pärinedes NUFORC-i andmebaasist.

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# Proovi teist mudelit
## Juhised

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# Ehita veebirakendus oma ML-mudeli kasutamiseks
Selles õppekava osas tutvustatakse teile rakenduslikku masinõppe teemat: kuidas salvestada oma Scikit-learn mudel failina, mida saab kasutada ennustuste tegemiseks veebirakenduses. Kui mudel on salvestatud, õpite, kuidas seda kasutada Flaskis ehitatud veebirakenduses. Kõigepealt loote mudeli, kasutades andmeid, mis käsitlevad UFO-vaatlusi! Seejärel ehitate veebirakenduse, mis võimaldab sisestada sekundite arvu koos laius- ja pikkuskraadi väärtustega, et ennustada, milline riik teatas UFO nägemisest.

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# Sissejuhatus klassifikatsiooni
Nendes neljas õppetunnis uurid klassikalise masinõppe põhivaldkonda - _klassifikatsiooni_. Vaatame erinevate klassifikatsioonialgoritmide kasutamist andmekogumiga, mis käsitleb Aasia ja India suurepäraseid kööke. Loodetavasti oled näljane!

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# Uurige klassifitseerimismeetodeid
## Juhised

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# Köögi klassifikaatorid 1
Selles tunnis kasutad eelmises tunnis salvestatud andmestikku, mis sisaldab tasakaalustatud ja puhastatud andmeid erinevate köökide kohta.

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# Uuri lahendajaid
## Juhised

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# Köögi klassifikaatorid 2
Selles teises klassifikatsiooniõppetunnis uurid rohkem viise, kuidas klassifitseerida numbrilisi andmeid. Samuti õpid, millised on tagajärjed ühe klassifikaatori valimisel teise asemel.

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# Parameetrite Mäng
## Juhised

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# Ehita köögi soovitaja veebirakendus
Selles õppetükis ehitad klassifikatsioonimudeli, kasutades mõningaid tehnikaid, mida oled õppinud eelnevates tundides, ja maitsvat köögidatasetit, mida on kasutatud kogu selle sarja jooksul. Lisaks ehitad väikese veebirakenduse, et kasutada salvestatud mudelit, kasutades Onnxi veebiruntime'i.

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# Loo soovitussüsteem
## Juhised

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# Klassifitseerimisega alustamine
## Piirkondlik teema: Maitsvad Aasia ja India köögid 🍜

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# Sissejuhatus klasterdamisse
Klasterdamine on [juhendamata õppe](https://wikipedia.org/wiki/Unsupervised_learning) tüüp, mis eeldab, et andmekogum on märgistamata või et selle sisendid ei ole seotud eelnevalt määratletud väljunditega. See kasutab erinevaid algoritme, et sorteerida märgistamata andmeid ja pakkuda rühmitusi vastavalt mustritele, mida ta andmetes tuvastab.

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# Uurige teisi visualiseerimisviise klasterdamiseks
## Juhised

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# K-Means klasterdamine
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ml/)

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# Proovi erinevaid klasterdamise meetodeid
## Juhised

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# Klasterdamise mudelid masinõppes
Klasterdamine on masinõppe ülesanne, mille eesmärk on leida objekte, mis sarnanevad üksteisele, ja rühmitada need klastriteks. Mis eristab klasterdamist teistest masinõppe lähenemistest, on see, et protsess toimub automaatselt – tegelikult võib öelda, et see on vastand juhendatud õppimisele.

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# Sissejuhatus loomuliku keele töötlemisse
See õppetund hõlmab lühikest ajalugu ja olulisi mõisteid *loomuliku keele töötlemisest*, mis on *arvutilingvistika* alavaldkond.

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# Otsi robotit
## Juhised

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# Looduslike keelte töötlemise (NLP) levinud ülesanded ja tehnikad
Enamiku *looduslike keelte töötlemise* ülesannete puhul tuleb töödeldav tekst jagada osadeks, analüüsida ja tulemused salvestada või reeglite ja andmekogumitega võrrelda. Need ülesanded võimaldavad programmeerijal tuletada teksti _tähendust_, _eesmärki_ või lihtsalt _sõnade ja terminite sagedust_.

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# Pane bot vastama
## Juhised

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# Tõlkimine ja meeleolu analüüs masinõppe abil
Eelnevates tundides õppisite, kuidas luua lihtsat robotit, kasutades `TextBlob`-i, raamatukogu, mis rakendab masinõpet kulisside taga, et täita põhilisi loomuliku keele töötlemise ülesandeid, nagu nimisõnafraaside tuvastamine. Üks oluline väljakutse arvutilingvistikas on täpne _tõlkimine_ ühest kõneldavast või kirjutatud keelest teise.

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# Poeetiline vabadus
## Juhised

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# Sentimentianalüüs hotelliarvustustega - andmete töötlemine
Selles osas kasutad eelmistes tundides õpitud tehnikaid, et teha suure andmestiku uurivat andmeanalüüsi. Kui oled saanud hea ülevaate erinevate veergude kasulikkusest, õpid:

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# NLTK
## Juhised

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# Sentimentaanalüüs hotelliarvustustega
Nüüd, kui olete andmestikku põhjalikult uurinud, on aeg filtreerida veerud ja kasutada NLP-tehnikaid, et saada hotellide kohta uusi teadmisi.

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# Proovi teistsugust andmekogumit
## Juhised

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# Loomulik keele töötlemisega alustamine
Loomulik keele töötlemine (NLP) on arvutiprogrammi võime mõista inimkeelt nii, nagu seda räägitakse ja kirjutatakse – viidates loomulikule keelele. See on tehisintellekti (AI) komponent. NLP on eksisteerinud üle 50 aasta ja selle juured ulatuvad lingvistika valdkonda. Kogu valdkond on suunatud masinate aitamisele inimkeele mõistmisel ja töötlemisel. Seda saab kasutada ülesannete täitmiseks, nagu õigekirjakontroll või masintõlge. Sellel on mitmesuguseid praktilisi rakendusi mitmes valdkonnas, sealhulgas meditsiiniuuringutes, otsingumootorites ja äriluures.

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Laadi hotelli ülevaate andmed sellesse kausta alla.
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# Sissejuhatus aegridade prognoosimisse
![Aegridade kokkuvõte visandina](../../../../translated_images/et/ml-timeseries.fb98d25f1013fc0c.webp)

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# Visualiseeri veel mõned ajaread
## Juhised

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# Ajaarvude prognoosimine ARIMA-ga
Eelmises õppetükis õppisite veidi ajaarvude prognoosimisest ja laadisite andmekogumi, mis näitab elektrikoormuse kõikumisi ajaperioodi jooksul.

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# Uus ARIMA mudel
## Juhised

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see on ajutine kohatäide
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# Ajasarja prognoosimine toetavate vektorite regressori abil
Eelmises õppetükis õppisite, kuidas kasutada ARIMA mudelit ajasarjade prognoosimiseks. Nüüd vaatame toetavate vektorite regressori mudelit, mis on regressioonimudel pidevate andmete ennustamiseks.

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# Uus SVR mudel
## Juhised [^1]

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# Sissejuhatus aegridade prognoosimisse
Mis on aegridade prognoosimine? See seisneb tulevaste sündmuste ennustamises, analüüsides mineviku trende.

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# Sissejuhatus tugevdusõppesse ja Q-õppesse
![Tugevdusõppe kokkuvõte masinõppes sketchnote'is](../../../../translated_images/et/ml-reinforcement.94024374d63348db.webp)

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# Realistlikum maailm
Meie olukorras suutis Peeter liikuda peaaegu väsimata või nälga tundmata. Realistlikumas maailmas peab ta aeg-ajalt istuma ja puhkama ning ka end toitma. Muudame oma maailma realistlikumaks, rakendades järgmisi reegleid:

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see on ajutine kohatäide
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# CartPole Uisutamine
Probleem, mida me eelmises tunnis lahendasime, võib tunduda mänguline ja mitte eriti elulähedane. Tegelikult see nii ei ole, sest paljud päriselulised probleemid jagavad sama stsenaariumi – näiteks malet või Go-d mängides. Need on sarnased, kuna meil on samuti mängulaud kindlate reeglitega ja **diskreetne olek**.

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# Treeni Mountain Car
[OpenAI Gym](http://gym.openai.com) on loodud nii, et kõik keskkonnad pakuvad sama API-d – st samu meetodeid `reset`, `step` ja `render` ning samu **tegevusruumi** ja **vaatlusruumi** abstraktsioone. Seetõttu peaks olema võimalik kohandada sama tugevdusõppe algoritmi erinevatele keskkondadele minimaalsete koodimuudatustega.

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# Sissejuhatus tugevdusõppesse
Tugevdusõpe, RL, on üks põhilisi masinõppe paradigmasid, kõrvuti juhendatud ja juhendamata õppega. RL keskendub otsustele: õigete otsuste tegemisele või vähemalt nende õppimisele.

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# Järelsõna: Masinõpe pärismaailmas
![Masinõppe kokkuvõte pärismaailmas sketšina](../../../../translated_images/et/ml-realworld.26ee274671615577.webp)

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# ML Aardejaht
## Juhised

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# Postscript: Masinõppe mudelite silumine vastutustundliku AI armatuurlaua komponentide abil
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ml/)

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# Uurige vastutustundliku tehisintellekti (RAI) juhtpaneeli
## Juhised

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# Järelsõna: Klassikalise masinõppe rakendused päriselus
Selles õppekava osas tutvustatakse teile klassikalise masinõppe rakendusi päriselus. Oleme internetist otsinud valgeid raamatuid ja artikleid, mis käsitlevad nende strateegiate kasutamist, vältides võimalusel närvivõrke, süvaõpet ja tehisintellekti. Uurige, kuidas masinõpet kasutatakse ärisüsteemides, ökoloogilistes rakendustes, rahanduses, kunstis ja kultuuris ning mujal.

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# AGENTS.md
## Projekti Ülevaade

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# Microsofti avatud lähtekoodi käitumisjuhend
See projekt on omaks võtnud [Microsofti avatud lähtekoodi käitumisjuhendi](https://opensource.microsoft.com/codeofconduct/).

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# Kaastöö tegemine
See projekt tervitab kaastöid ja ettepanekuid. Enamik kaastöid nõuab, et te nõustuksite Kaastöö Litsentsilepinguga (CLA), mis kinnitab, et teil on õigus ja te tegelikult annate meile õiguse teie kaastööd kasutada. Lisateabe saamiseks külastage https://cla.microsoft.com.

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[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
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[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
### 🌐 Mitmekeelne tugi
### 🌐 Mitmekeelsuse tugi
#### Toetatud GitHub Actioni kaudu (automatiseeritud & alati värske)
#### Toetatud GitHub Actioni kaudu (automatiseeritud ja alati ajakohane)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](./README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
[Araabia](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgaaria](../bg/README.md) | [Burma (Myanmar)](../my/README.md) | [Hiina (lihtsustatud)](../zh-CN/README.md) | [Hiina (traditsiooniline, Hongkong)](../zh-HK/README.md) | [Hiina (traditsiooniline, Macao)](../zh-MO/README.md) | [Hiina (traditsiooniline, Taiwan)](../zh-TW/README.md) | [Horvaadi](../hr/README.md) | [Tšehhi](../cs/README.md) | [Taani](../da/README.md) | [Hollandi](../nl/README.md) | [Eesti](./README.md) | [Soome](../fi/README.md) | [Prantsuse](../fr/README.md) | [Saksa](../de/README.md) | [Kreeka](../el/README.md) | [Heebrea](../he/README.md) | [Hindi](../hi/README.md) | [Ungari](../hu/README.md) | [Indoneesia](../id/README.md) | [Itaalia](../it/README.md) | [Jaapani](../ja/README.md) | [Kannada](../kn/README.md) | [Korea](../ko/README.md) | [Leedu](../lt/README.md) | [Malai](../ms/README.md) | [Malajalami](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigeeria pidgin](../pcm/README.md) | [Norra](../no/README.md) | [Pärsia (Farsi)](../fa/README.md) | [Poola](../pl/README.md) | [Portugali (Brasiilia)](../pt-BR/README.md) | [Portugali (Portugal)](../pt-PT/README.md) | [Pandžabi (Gurmukhi)](../pa/README.md) | [Rumeenia](../ro/README.md) | [Vene](../ru/README.md) | [Serbia (kirilitsa)](../sr/README.md) | [Sloveenia](../sk/README.md) | [Sloveeni](../sl/README.md) | [Hispaania](../es/README.md) | [Suaali](../sw/README.md) | [Rootsi](../sv/README.md) | [Tagalogi (filipino)](../tl/README.md) | [Tamili](../ta/README.md) | [Telugu](../te/README.md) | [Tai](../th/README.md) | [Türgi](../tr/README.md) | [Ukraina](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnam](../vi/README.md)
> **Eelistad kloonida kohapeal?**
> **Eelistate kloonida kohapeal?**
> See hoidla sisaldab üle 50 keele tõlkeid, mis suurendab märkimisväärselt allalaadimise mahtu. Tõlgeteta kloonimiseks kasuta harvendatud (sparse) checkouti:
> See hoidla sisaldab 50+ keele tõlget, mis suurendab oluliselt allalaaditava faili suurust. Tõlgeteta kloonimiseks kasutage hajutatud välja kontrolli:
> ```bash
> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
> cd ML-For-Beginners
> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
> ```
> See annab sulle kõik vajaliku kursuse läbimiseks palju kiirema allalaadimisega.
> See annab teile kõik, mida on vaja kursuse läbimiseks palju kiirema allalaadimisega.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
#### Liitu meie kogukonnaga
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Meil on käimas Discordi õppesari "Õpi koos tehisintellektiga", uuri lisa ja liitu meiega aadressil [Learn with AI Series](https://aka.ms/learnwithai/discord) ajavahemikus 18. - 30. september 2025. Saad saada vihjeid ja nippe GitHub Copiloti kasutamiseks andmeteaduses.
Meil on käimas Discordi õppesari tehisintellektiga, lisateavet ja liitumisvõimalusi leiate aadressilt [Learn with AI Series](https://aka.ms/learnwithai/discord) ajavahemikul 18.-30. september 2025. Saate näpunäiteid ja nippe GitHub Copilot’i kasutamiseks andmeteaduses.
![Learn with AI series](../../../../translated_images/et/3.9b58fd8d6c373c20.webp)
![Õpi AI-ga sari](../../translated_images/et/3.9b58fd8d6c373c20.webp)
# Masinõpe algajatele - õppekava
# Masinõpe algajatele – õppekava
> 🌍 Rändame mööda maailma, uurides masinõpet erinevate maailmakultuuride kaudu 🌍
> 🌍 Rändame mööda maailma, uurides masinõpet maailma kultuuride kaudu 🌍
Microsofti pilveettekandjate meeskond pakub 12-nädalast, 26-õppetunnist koosnevat õppekava, mis keskendub **masinõppele**. Selles õppekavas õpid nn **klassikalist masinõpet**, kasutades peamiselt Scikit-learn kauplust ning vältides süvaõpet, mida käsitletakse meie [tehisintellekti algajatele õppekavas](https://aka.ms/ai4beginners). Paarita need õppetunnid ka meie ['Andmeteaduse algajatele' õppekavaga](https://aka.ms/ds4beginners)!
Microsofti pilvekaitsjad on rõõmsad pakkuda 12-nädalast, 26-õppeainelist õppekava kogu teemal **Masinõpe**. Selles õppekavas õpite tundma seda, mida mõnikord nimetatakse **klassikaliseks masinõppeks**, kasutades peamiselt Scikit-learn teeki ja vältides süvaõpet, mis on kaetud meie [AI algajate õppekavas](https://aka.ms/ai4beginners). Paarige need õppetunnid ka meie ['Andmeteaduse algajate õppekavaga'](https://aka.ms/ds4beginners)!
Rändame koos üle maailma, rakendades neid klassikalisi meetodeid erinevate maailma piirkondade andmetele. Iga õppetund sisaldab eelkatsed ja järeltöötluskatsed, kirjalikud juhised õppetöö lõpetamiseks, lahenduse, tööülesande ja veelgi rohkem. Meie projektipõhine pedagoogika võimaldab sul õppida ehitades, mis on tõestatud viis uute oskuste kinnistamiseks.
Rändage meiega mööda maailma, rakendades neid klassikalisi tehnikaid andmetele mitmest maailma piirkonnast. Igas õppetükis on eelmise ja järgse kontrollküsimustik, kirjalikud juhised ülesande täitmiseks, lahendus, ülesanne ja palju muud. Meie projektipõhine pedagoogika võimaldab teil õppida ehitades, mis on hästi tõestatud uusate oskuste kinnistamisel.
**✍️ Südamlik tänu meie autoritele** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu ja Amy Boyd
**🎨 Tänud ka meie illustraatoritele** Tomomi Imura, Dasani Madipalli ja Jen Looper
**🙏 Eriline tänu 🙏 meie Microsofti õpilas-suursaadikute autoritele, arvustajatele ja sisuloojatele**, eriti Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila ja Snigdha Agarwal
**🙏 Eriline tänu 🙏 meie Microsofti tudengisaadikute autoritele, retsensentidele ja sisukorraldajatele**, eelkõige Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila ja Snigdha Agarwal
**🤩 Täiendav tänu Microsofti õpilas-suursaadikutele Eric Wanjau, Jasleen Sondhi ja Vidushi Gupta meie R-õppetundide eest!**
**🤩 Suur tänu Microsofti tudengisaadikutele Eric Wanjau, Jasleen Sondhi ja Vidushi Gupta meie R-õppetundide eest!**
# Alustamine
# Algus
Järgi neid samme:
1. **Tee hoidla fork**: kliki selle lehe paremas ülanurgas olevale nupule „Fork“.
2. **Klooni hoidla**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
Järgige neid samme:
1. **Tee hoidlast haru (fork)**: Vajutage selle lehe paremas ülanurgas olevale nupule "Fork".
2. **Kloonige hoidla**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
> [leiad kõik täiendavad selle kursuse ressursid meie Microsoft Learn kollektsioonist](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
> [Leia kõik selle kursuse lisamaterjalid meie Microsoft Learn kogust](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
> 🔧 **Vajad abi?** Vaata meie [Tõrkeotsingu juhendit](TROUBLESHOOTING.md), kus on lahendused levinud probleemidele paigaldamisel, häälestamisel ja õppetundide käivitamisel.
> 🔧 **Vaja abi?** Vaadake meie [probleemide lahendamise juhendit](TROUBLESHOOTING.md) sageli esinevate paigaldus-, seadistus- ja õppetundide käivitamise probleemide kohta.
**[Õpilased](https://aka.ms/student-page)**, selle õppekava kasutamiseks tehke kogu hoidlast haru enda GitHubi kontole ja täitke harjutused üksi või grupis:
**[Õpilased](https://aka.ms/student-page)**, selle õppekava kasutamiseks tee fork kogu hoidlast omaenda GitHubi kontole ja lõpeta harjutused iseseisvalt või grupiga:
- Alustage eeloenguküsimustikuga.
- Loe õppetundi ja täida tegevused, peatudes ja mõeldes igal teadmiste kontrollil.
- Proovige projekte luua õppetunde mõistes, mitte lahenduskoodi jooksutades; kuid see kood on olemas iga projekti suunitlusega õppetunni `/solution` kaustas.
- Tehke järelkontrollküsimustik.
- Täitke väljakutse.
- Täitke ülesanne.
- Pärast õppetükkide rühma lõpetamist külastage [arutelufoorumit](https://github.com/microsoft/ML-For-Beginners/discussions) ja "õppige valjult", täites vastava PAT hindamisvahendi rubriigi. 'PAT' on edenemise hindamise tööriist, mida täites süvendate oma õppimist. Võite ka teisi PAT-e kommenteerida, et koos õppida.
- Alusta eelkatsest.
- Loe loeng ja lõpeta tegevused, peatu ja mõtiskle igal teadmiste kontrollimisel.
- Püüa luua projektid, mõistes õppetunde, selle asemel et koodi kopeerida; kuid see kood on olemas iga projektimeelse õppetunni /solution kaustas.
- Tee pärast loengut test.
- Tee väljakutse.
- Tee tööülesanne.
- Pärast õppetunde rühma lõpetamist külasta [Arutelufoorumit](https://github.com/microsoft/ML-For-Beginners/discussions) ja „õpi valjusti“ läbides sobiva PAT hindamislehe. PAT on edenemise hindamiseks kasutatav rubriik, mida täites oma õppimist toetad. Samuti võid reageerida teiste PATidele, et koos õppida.
> Edasiseks õppimiseks soovitame järgida neid [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) mooduleid ja õpiteid.
> Täiendavaks õppeks soovitame järgida neid [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) mooduleid ja õpiteid.
**Õpetajad**, meil on [mõningaid soovitusi](for-teachers.md), kuidas seda õppekava kasutada.
**Õpetajad**, oleme lisanud [soovitusi](for-teachers.md), kuidas seda õppekava kasutada.
---
## Videokäivitused
## Videod
Mõned õppetunnid on saadaval lühikeste videotena. Leiad need sisseehitatult õppetundidest või [ML algajatele esitusloendist Microsofti arendaja YouTube kanalil](https://aka.ms/ml-beginners-videos) pildile klõpsates.
Mõned õppetunnid on saadaval lühivideotena. Leiate need kõik õppetundide seest või [ML algajate esitusloendist Microsoft Developer YouTube kanalil](https://aka.ms/ml-beginners-videos) alloleva pildi kaudu.
[![ML for beginners banner](../../../../translated_images/et/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
[![ML algajatele bänner](../../translated_images/et/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
---
## Tutvu meeskonnaga
## Kohtuge meeskonnaga
[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
**Gif autor** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
> 🎥 Klõpsa ülalolevale pildile, et vaadata videot projektist ja inimestest, kes selle lõid!
> 🎥 Klõpsake ülalolevat pilti, et vaadata videot projektist ja selle loojatest!
---
## Pedagoogika
Selle õppekava loomisel oleme valinud kaks pedagoogilist printsiipi: tagada, et see on praktiline **projektipõhine** ja sisaldab **sagedasi teste**. Lisaks on sellel õppekaval ühine **teema**, mis annab sellele sidususe.
Selle õppekava koostamisel oleme valinud kaks pedagoogilist põhimõtet: tagada see käed-küljes **projektipõhisus** ja et see sisaldab **sagedasi küsimustikke**. Lisaks on õppekaval ühine **teema**, mis annab sellele ühtsuse.
Sisule projektide kaudu sobitamisega muudetakse protsess õppuritele huvitavamaks ja kontseptsioonide kinnistamine tugevamaks. Lisaks seab madala panusega eelkats enne tundi õppuri kavatsuse teemat omandada, samas kui teine kats tunnijärgselt tagab teadmiste kinnistamise. See õppekava on loodud olema paindlik ja lõbus ning seda saab läbida tervikuna või ositi. Projektid algavad väikestena ja muutuvad 12-nädalase tsükli lõpus järjest keerukamaks. Õppekava sisaldab ka järelsõna masinõppe reaalse maailma rakendustest, mida võib kasutada lisapunktide saamiseks või arutelupõhjana.
Tagades sisu ühtesobivuse projektidega, muutub protsess õpilaste jaoks kaasahaaravamaks ja kontseptsioonide omandamine paraneb. Lisaks seab madala panusega testi tegemine enne tundi õppija kavatsuse õppimisele, samas kui teine test pärast tundi kindlustab veelgi parema omandamise. See õppekava on kavandatud paindlikuks ja lõbusaks ning seda saab võtta tervikuna või osadena. Projektid algavad väikestena ja muutuvad 12-nädalase tsükli lõpus järjest keerukamaks. Käesolev õppekava sisaldab ka lisamärkusi masinõppe reaalse maailma rakenduste kohta, mida võib kasutada lisapunktide saamiseks või arutelupõhjana.
> Leia meie [käitumiskoodeks](CODE_OF_CONDUCT.md), [panustamine](CONTRIBUTING.md), [tõlkimine](TRANSLATIONS.md) ja [tõrkeotsing](TROUBLESHOOTING.md) juhendid. Ootame konstruktiivset tagasisidet!
> Leidke meie [käitumisreeglid](CODE_OF_CONDUCT.md), [panustamise juhised](CONTRIBUTING.md), [tõlkimise juhendid](TRANSLATIONS.md) ja [probleemide lahendamise juhend](TROUBLESHOOTING.md). Ootame teie konstruktiivset tagasisidet!
## Iga õppetund sisaldab
## Igas õppetükis on
- vabatahtlik sketšmärkmed
- vabatahtlik visand
- vabatahtlik täiendav video
- videotutvustus (ainult mõni õppetund)
- [eelloengu soojenduskatse](https://ff-quizzes.netlify.app/en/ml/)
- videokäik (ainult mõnes õppetükis)
- [eelloengu soojendus-Test](https://ff-quizzes.netlify.app/en/ml/)
- kirjalik õppetund
- projektipõhiste õppetundide puhul samm-sammulised juhised, kuidas projekt üles ehitada
- projektipõhiste õppetundide puhul samm-sammult juhendid projekti ülesehitamiseks
- teadmiste kontrollid
- väljakutse
- täiendav lugemine
- tööülesanne
- [järguloengu test](https://ff-quizzes.netlify.app/en/ml/)
> **Märkus keeltest**: Need õppetunnid on peamiselt kirjutatud Pythoni keeles, kuid paljud on saadaval ka R-s. R-õppetunni läbimiseks mine /solution kausta ja otsi R õppetunde. Need sisaldavad .rmd laiendit, mis tähistab **R Markdowni** faili, mida võib defineerida kui `koodiblokkide` (R või teiste keelte) ja `YAML päise` (mis juhib väljundite vormindamist, nt PDF) kombineerimist Markdown dokumendis. See pakub suurepärast raamistikku andmeteaduseks, sest võimaldab kombineerida koodi, selle väljundit ja mõtteid, mille saab kirja panna Markdownis. Lisaks saab R Markdowni dokumente renderdada väljundvormingutesse nagu PDF, HTML või Word.
> **Märkus viktoriinide kohta**: Kõik viktoriinid asuvad [Quiz App kaustas](../../quiz-app), kokku 52 viktoriini, milles igas on kolm küsimust. Need on õppetundidesse lingitud, kuid viktoriini rakendust saab käivitada lokaalselt; juhised lokaalseks majutamiseks või Azure'i kasutuselevõtuks leiate `quiz-app` kaustast.
| Õppetunni number | Teema | Õppetunni grupp | Õpieesmärgid | Link õppetundile | Autor |
| :--------------: | :------------------------------------------------------------: | :-------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: |
| 01 | Masinõppe tutvustus | [Sissejuhatus](1-Introduction/README.md) | Õpi masinõppe põhikontseptsioone | [Õppetund](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | Masinõppe ajalugu | [Sissejuhatus](1-Introduction/README.md) | Õpi selle valdkonna ajalugu | [Õppetund](1-Introduction/2-history-of-ML/README.md) | Jen ja Amy |
| 03 | Õiglus ja masinõpe | [Sissejuhatus](1-Introduction/README.md) | Millised on olulised filosoofilised küsimused õiglusest, mida õppijad peaksid kaaluma masinõppemudelite loomisel ja kasutamisel? | [Õppetund](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Masinõppe tehnikad | [Sissejuhatus](1-Introduction/README.md) | Milliseid tehnikaid masinõppe uurijad kasutavad mudelite loomiseks? | [Õppetund](1-Introduction/4-techniques-of-ML/README.md) | Chris ja Jen |
| 05 | Regresseerimise tutvustus | [Regressioon](2-Regression/README.md) | Alusta Pythoniga ja Scikit-learniga regressioonimudelite jaoks | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau |
| 06 | Põhja-Ameerika kõrvitsa hinnad 🎃 | [Regressioon](2-Regression/README.md) | Visualiseeri ja puhasta andmeid masinõppe ettevalmistamiseks | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau |
| 07 | Põhja-Ameerika kõrvitsa hinnad 🎃 | [Regressioon](2-Regression/README.md) | Ehita lineaar- ja polünoomregressioonimudelid | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen ja Dmitry • Eric Wanjau |
| 08 | Põhja-Ameerika kõrvitsa hinnad 🎃 | [Regressioon](2-Regression/README.md) | Ehita logistilise regressiooni mudel | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau |
| 09 | Veebirakendus 🔌 | [Veebirakendus](3-Web-App/README.md) | Ära oma koolitatud mudeliga veebirakendus | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Klassifitseerimise tutvustus | [Klassifitseerimine](4-Classification/README.md) | Puhasta, valmista ette ja visualiseeri oma andmeid; klassifitseerimise tutvustus | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen ja Cassie • Eric Wanjau |
| 11 | Maitsvad Aasia ja India toidud 🍜 | [Klassifitseerimine](4-Classification/README.md) | Klassifitseerijate tutvustus | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen ja Cassie • Eric Wanjau |
| 12 | Maitsvad Aasia ja India toidud 🍜 | [Klassifitseerimine](4-Classification/README.md) | Rohkem klassifitseerijaid | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen ja Cassie • Eric Wanjau |
| 13 | Maitsvad Aasia ja India toidud 🍜 | [Klassifitseerimine](4-Classification/README.md) | Ehita mudelit kasutav soovitusrakendus | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Klasterdamise tutvustus | [Klasterdamine](5-Clustering/README.md) | Puhasta, valmista ette ja visualiseeri oma andmeid; klasterdamise tutvustus | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau |
| 15 | Nigeeria muusikamaitsete uurimine 🎧 | [Klasterdamine](5-Clustering/README.md) | Uuri K-keskmiste klasterdamismeetodit | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau |
| 16 | Loodusliku keele töötlemise tutvustus ☕️ | [Loodusliku keele töötlemine](6-NLP/README.md) | Õpi NLP põhialuseid, ehitades lihtsa boti | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Levinumad NLP ülesanded ☕️ | [Loodusliku keele töötlemine](6-NLP/README.md) | Süvenda NLP teadmisi, mõistes keelestruktuuridega töötamiseks vajalikke ülesandeid | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Tõlge ja emotsioonianalüüs ♥️ | [Loodusliku keele töötlemine](6-NLP/README.md) | Tõlge ja emotsioonianalüüs koos Jane Austeniga | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Euroopa romantilised hotellid ♥️ | [Loodusliku keele töötlemine](6-NLP/README.md) | Emotsioonianalüüs hotellikriitikatega 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Euroopa romantilised hotellid ♥️ | [Loodusliku keele töötlemine](6-NLP/README.md) | Emotsioonianalüüs hotellikriitikatega 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Ajajoonte prognoosimise tutvustus | [Ajajoons](7-TimeSeries/README.md) | Ajajoonte prognoosimise tutvustus | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ Maailma energiatarve ⚡️ - ajajoonte prognoos ARIMA kaudu | [Ajajoons](7-TimeSeries/README.md) | Ajajoonte prognoos ARIMA mudeliga | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ Maailma energiatarve ⚡️ - ajajoonte prognoos SVRiga | [Ajajoons](7-TimeSeries/README.md) | Ajajoonte prognoos Toetava Vektori Regresseerijaga | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Tugevdusõppe tutvustus | [Tugevdusõpe](8-Reinforcement/README.md) | Tutvustus tugevdusõppele Q-õppe kaudu | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Aidake Peteril hundi eest põgeneda! 🐺 | [Tugevdusõpe](8-Reinforcement/README.md) | Tugevdusõppe Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Lisapeatükid | Masinõppe reaalsed stsenaariumid ja rakendused | [Masinõpe vabas looduses](9-Real-World/README.md) | Huvitavad ja valgustavad reaalsed masinõppe rakendused | [Õppetund](9-Real-World/1-Applications/README.md) | Meeskond |
| Lisapeatükid | Mudelite silumine ML-s RAI juhtpaneeli abil | [Masinõpe vabas looduses](9-Real-World/README.md) | Masinõppe mudelite silumine kasutades Responsible AI juhtpaneeli komponente | [Õppetund](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu |
> [leia kõik selle kursuse täiendavad ressursid meie Microsoft Learni kogumikust](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
## Võrguühenduseta kasutamine
Seda dokumentatsiooni saab kasutada võrguühenduseta, kasutades [Docsify](https://docsify.js.org/#/). Hargi see repositorium, [installi Docsify](https://docsify.js.org/#/quickstart) oma kohalikule masinale ja seejärel selle repositoriumi juurkaustas kirjuta `docsify serve`. Veebisait serverdatakse pordil 3000 sinu lokaalses hostis: `localhost:3000`.
- ülesanne
- [järgnev testi sooritamine](https://ff-quizzes.netlify.app/en/ml/)
> **Märkus keeltest**: Need õppetunnid on peamiselt kirjutatud Pythonis, kuid paljud on saadaval ka R keeles. R-õppetunni lõpuleviimiseks minge `/solution` kausta ja otsige R-õppetunde. Neil on .rmd laiendus, mis tähistab **R Markdown** faili – see on lihtsustatult öeldes `koodi plokkide` (R-i või teiste keelte) ja `YAML päise` (mis juhib väljundi vormindamist nagu PDF) manustamine `Markdown` dokumenti. Seega on see eeskujulik raamistik andmeteaduslike dokumentide koostamiseks, kuna võimaldab kombineerida oma koodi, selle väljundi ja mõtted, kirjutades need maha Markdownis. Lisaks saab R Markdown dokumente renderdada sellistesse väljundformaati nagu PDF, HTML või Word.
> **Märkus viktoriinide kohta**: Kõik viktoriinid asuvad [Quiz App kaustas](../../quiz-app), kokku 52 viktoriini, igaüks kolme küsimusega. Need on õppetundide sees linkidena, kuid viktoriini rakendust saab käivitada ka lokaalselt; järgige juhiseid kaustas `quiz-app`, et seda lokaalselt hostida või Azure’i juurutada.
| Õppetunni number | Teema | Õppetunni grupp | Õpiväljundid | Lingitud õppetund | Autor |
| :--------------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: |
| 01 | Sissejuhatus masinõppesse | [Sissejuhatus](1-Introduction/README.md) | Õpi masinõppe põhikontseptsioone | [Õppetund](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | Masinõppe ajalugu | [Sissejuhatus](1-Introduction/README.md) | Õpi selle valdkonna ajaloo kohta | [Õppetund](1-Introduction/2-history-of-ML/README.md) | Jen ja Amy |
| 03 | Õiglus ja masinõpe | [Sissejuhatus](1-Introduction/README.md) | Millised on olulised õiglusfilosoofilised küsimused, mida õpilased peaksid arvestama masinõppemudelite loomisel ja rakendamisel?| [Õppetund](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Masinõppe tehnikad | [Sissejuhatus](1-Introduction/README.md) | Milliseid tehnikaid kasutavad masinõppeteadlased mudelite loomiseks? | [Õppetund](1-Introduction/4-techniques-of-ML/README.md) | Chris ja Jen |
| 05 | Sissejuhatus regressiooni | [Regressioon](2-Regression/README.md) | Alusta Pythoni ja Scikit-learniga regressioonimudelite jaoks | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau |
| 06 | Põhja-Ameerika kõrvitsa hinnad 🎃 | [Regressioon](2-Regression/README.md) | Andmete visualiseerimine ja korrastamine masinõppeks | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau |
| 07 | Põhja-Ameerika kõrvitsa hinnad 🎃 | [Regressioon](2-Regression/README.md) | Lineaarsete ja polünoomsete regressioonimudelite loomine | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen ja Dmitry • Eric Wanjau |
| 08 | Põhja-Ameerika kõrvitsa hinnad 🎃 | [Regressioon](2-Regression/README.md) | Logistilise regressioonimudeli loomine | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau |
| 09 | Veebirakendus 🔌 | [Veebirakendus](3-Web-App/README.md) | Loo veebirakendus oma väljaõppinud mudeli kasutamiseks | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Sissejuhatus klassifikatsiooni | [Klassifikatsioon](4-Classification/README.md) | Puhasta, valmista ette ja visualiseeri oma andmeid; sissejuhatus klassifikatsiooni | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen ja Cassie • Eric Wanjau |
| 11 | Maitsvad Aasia ja India köögid 🍜 | [Klassifikatsioon](4-Classification/README.md) | Sissejuhatus klassifikaatoritesse | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen ja Cassie • Eric Wanjau |
| 12 | Maitsvad Aasia ja India köögid 🍜 | [Klassifikatsioon](4-Classification/README.md) | Rohkem klassifikaatoreid | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen ja Cassie • Eric Wanjau |
| 13 | Maitsvad Aasia ja India köögid 🍜 | [Klassifikatsioon](4-Classification/README.md) | Ehita soovitusrakendus veebis, kasutades oma mudelit | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Sissejuhatus klasterdamisse | [Klasterdamine](5-Clustering/README.md) | Puhasta, valmista ette ja visualiseeri oma andmeid; sissejuhatus klasterdamisse | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau |
| 15 | Nigeeria muusikamaitse uurimine 🎧 | [Klasterdamine](5-Clustering/README.md) | Uuri K-Meansi klasterdamismeetodit | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau |
| 16 | Sissejuhatus loomuliku keele töötlemisse ☕️ | [Loomuliku keele töötlemine](6-NLP/README.md) | Õpi loomuliku keele töötlemise põhialuseid, luues lihtsa boti | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Üldised NLP ülesanded ☕️ | [Loomuliku keele töötlemine](6-NLP/README.md) | Süvenda oma NLP teadmisi, mõistes keele struktuuride puhul vajalikke tavaülesandeid | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Tõlkimine ja meeleolu analüüs ♥️ | [Loomuliku keele töötlemine](6-NLP/README.md) | Tõlkimine ja meeleolu analüüs koos Jane Austeniga | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantilised hotellid Euroopas ♥️ | [Loomuliku keele töötlemine](6-NLP/README.md) | Meeleolu analüüs hotellide arvustustega 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantilised hotellid Euroopas ♥️ | [Loomuliku keele töötlemine](6-NLP/README.md) | Meeleolu analüüs hotellide arvustustega 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Sissejuhatus ajaseeria prognoosimisse | [Ajaseeria](7-TimeSeries/README.md) | Sissejuhatus ajaseeria prognoosimisse | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ Maailma energiatarbimine ⚡️ - ajaseeria prognoos ARIMAga | [Ajaseeria](7-TimeSeries/README.md) | Ajaseeria prognoosimine ARIMA mudeliga | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ Maailma energiatarbimine ⚡️ - ajaseeria prognoos SVR-ga | [Ajaseeria](7-TimeSeries/README.md) | Ajaseeria prognoosimine toetatava vektorregressori abil | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Sissejuhatus tugevdatud õppimisse | [Tugevdatud õppimine](8-Reinforcement/README.md) | Sissejuhatus tugevdatud õppimisse Q-õppega | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Aita Peteril hundi eest põgeneda! 🐺 | [Tugevdatud õppimine](8-Reinforcement/README.md) | Tugevdatud õppimise Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Järelkiri | Reaalsed masinõppe stsenaariumid ja rakendused | [ML looduses](9-Real-World/README.md) | Huvitavad ja paljastavad klassikalise masinõppe reaalsed rakendused | [Õppetund](9-Real-World/1-Applications/README.md) | Meeskond |
| Järelkiri | Mudelite silumine masinõppes kasutades RAI armatuurlaua | [ML looduses](9-Real-World/README.md) | Masinõppe mudelite silumine vastutustundliku AI armatuulaua komponentidega | [Õppetund](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu |
> [Leia selle kursuse kõik lisamaterjalid meie Microsoft Learn kollektsioonist](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
## Võimalus võrguühenduseta kasutamiseks
Seda dokumentatsiooni saab kasutada ka võrguühenduseta, kasutades [Docsify](https://docsify.js.org/#/). Hargne see hoidla, [paigalda Docsify](https://docsify.js.org/#/quickstart) oma kohalikku masinasse ja seejärel kirjelda selle hoidla juurkaustas käsk `docsify serve`. Veebisait käivitatakse pordil 3000 sinu lokaalarvutis: `localhost:3000`.
## PDF-id
Leia õppekava pdf koos linkidega [siit](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
Leia õppekava pdf-vormingus koos linkidega [siin](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
## 🎒 Muud kursused
Meie meeskond toodab ka teisi kursuseid! Vaata lähemalt:
Meie meeskond toodab ka teisi kursuseid! Vaata:
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
@ -181,7 +171,7 @@ Meie meeskond toodab ka teisi kursuseid! Vaata lähemalt:
---
### Azure / Edge / MCP / Agendid
### Azure / Edge / MCP / Agentid
[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
@ -189,44 +179,44 @@ Meie meeskond toodab ka teisi kursuseid! Vaata lähemalt:
---
### Generatiivne tehisintellekt seeria
[![Generatiivne tehisintellekt algajatele](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
### Generatiivse tehisintellekti sari
[![Sügavõppimine algajatele](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
[![Generatiivne tehisintellekt (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
[![Generatiivne tehisintellekt (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
[![Generatiivne tehisintellekt (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
---
### Põhijuhendamine
[![Masinõpe algajatele](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
### Põhiline õppimine
[![ML algajatele](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
[![Andmeteadus algajatele](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
[![Tehisintellekt algajatele](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
[![Küberjulgeolek algajatele](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
[![Veebiarendus algajatele](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
[![Asjade internet algajatele](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
[![XR-arendus algajatele](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
[![IoT algajatele](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
[![XR arendus algajatele](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
---
### Copiloti sari
[![Copilot AI-paarprogrammeerimiseks](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
### Copiloti seeria
[![Copilot tehisintellekti paarisprogrammeerimiseks](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
[![Copilot C#/.NET jaoks](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
[![Copiloti seiklused](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
<!-- CO-OP TRANSLATOR OTHER COURSES END -->
## Abi saamine
Kui jääd kinni või sul on küsimusi tehisintellekti rakenduste loomise kohta, liitu teiste õppijate ja kogenud arendajatega MCP aruteludes. See on toetav kogukond, kus küsimusi oodatakse ja teadmisi jagatakse vabalt.
Kui jääd hätta või sul on küsimusi tehisintellekti rakenduste loomise kohta, ühine teiste õppijate ja kogenud arendajatega MCP aruteludes. See on toetav kogukond, kus küsimusi on teretulnud ja teadmisi jagatakse vabalt.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Kui sul on toote tagasisidet või ehitamisel vigu, külasta:
Kui sul on toote tagasisidet või ehitamise ajal vigu, külasta:
[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
---
<!-- CO-OP TRANSLATOR DISCLAIMER START -->
**Vastutusest loobumine**:
See dokument on tõlgitud kasutades tehisintellektil põhinevat tõlketeenust [Co-op Translator](https://github.com/Azure/co-op-translator). Kuigi püüame tagada täpsuse, palun arvestage, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokument selle emakeeles tuleb pidada autoriteetseks allikaks. Tähtsa teabe puhul soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tingitud arusaamatuste või valesti mõistmiste eest.
**Ei vastuta**:
See dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame täpsust, palun pidage meeles, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokument selle emakeeles tuleks lugeda autoriteetse allikana. Olulise teabe puhul soovitatakse professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste ega valesti mõistmiste eest.
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## Turvalisus

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# Tugi
## Kuidas esitada probleeme ja saada abi

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# Tõrkeotsingu juhend
See juhend aitab lahendada levinumaid probleeme, mis võivad tekkida algajatele mõeldud masinõppe õppekava kasutamisel. Kui siit lahendust ei leia, vaadake meie [Discordi arutelusid](https://aka.ms/foundry/discord) või [avage probleem](https://github.com/microsoft/ML-For-Beginners/issues).

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- Sissejuhatus
- [Masinõppe sissejuhatus](../1-Introduction/1-intro-to-ML/README.md)
- [Masinõppe ajalugu](../1-Introduction/2-history-of-ML/README.md)

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## Õpetajatele
Kas soovite seda õppekava oma klassis kasutada? Palun tehke seda julgelt!

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# Viktoriinid
Need viktoriinid on masinõppe õppekava eel- ja järelloengute viktoriinid aadressil https://aka.ms/ml-beginners

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Attribution-ShareAlike 4.0 Rahvusvaheline
=======================================================================

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"translation_date": "2025-10-11T11:24:21+00:00",
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Kõik õppekava visuaalsed märkmed saab alla laadida siit.
🖨 Kõrglahutusega printimiseks on TIFF-versioonid saadaval [selles repos](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff).

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