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# Introduktion til maskinlæring
## [Quiz før lektionen](https://ff-quizzes.netlify.app/en/ml/)

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# Kom i gang
## Instruktioner

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# Historien om maskinlæring
![Oversigt over historien om maskinlæring i en sketchnote](../../../../sketchnotes/ml-history.png)

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# Opret en tidslinje
## Instruktioner

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# Bygge maskinlæringsløsninger med ansvarlig AI
![Oversigt over ansvarlig AI i maskinlæring i en sketchnote](../../../../sketchnotes/ml-fairness.png)

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# Udforsk Responsible AI Toolbox
## Instruktioner

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# Teknikker inden for maskinlæring
Processen med at opbygge, bruge og vedligeholde maskinlæringsmodeller og de data, de anvender, adskiller sig markant fra mange andre udviklingsarbejdsgange. I denne lektion vil vi afmystificere processen og skitsere de vigtigste teknikker, du skal kende. Du vil:

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# Interview en data scientist
## Instruktioner

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# Introduktion til maskinlæring
I denne del af pensum vil du blive introduceret til de grundlæggende begreber inden for maskinlæring, hvad det er, og lære om dets historie samt de teknikker, forskere bruger til at arbejde med det. Lad os udforske denne nye verden af ML sammen!

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# Kom godt i gang med Python og Scikit-learn til regressionsmodeller
![Oversigt over regressioner i en sketchnote](../../../../sketchnotes/ml-regression.png)

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# Regression med Scikit-learn
## Instruktioner

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# Byg en regressionsmodel med Scikit-learn: forbered og visualiser data
![Data visualisering infographic](../../../../2-Regression/2-Data/images/data-visualization.png)

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# Udforskning af Visualiseringer
Der findes flere forskellige biblioteker til datavisualisering. Lav nogle visualiseringer ved hjælp af Græskar-dataene i denne lektion med matplotlib og seaborn i en prøve-notebook. Hvilke biblioteker er nemmest at arbejde med?

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# Byg en regressionsmodel med Scikit-learn: regression på fire måder
![Infografik om lineær vs. polynomisk regression](../../../../2-Regression/3-Linear/images/linear-polynomial.png)
@ -114,11 +105,11 @@ Nu hvor du har en forståelse af matematikken bag lineær regression, lad os opr
Fra den forrige lektion har du sandsynligvis set, at gennemsnitsprisen for forskellige måneder ser sådan ud:
<img alt="Gennemsnitspris pr. måned" src="../2-Data/images/barchart.png" width="50%"/>
<img alt="Gennemsnitspris pr. måned" src="../../../../translated_images/da/barchart.a833ea9194346d76.webp" width="50%"/>
Dette antyder, at der bør være en vis korrelation, og vi kan prøve at træne en lineær regressionsmodel til at forudsige forholdet mellem `Month` og `Price`, eller mellem `DayOfYear` og `Price`. Her er scatterplottet, der viser sidstnævnte forhold:
<img alt="Scatterplot af pris vs. dag i året" src="images/scatter-dayofyear.png" width="50%" />
<img alt="Scatterplot af pris vs. dag i året" src="../../../../translated_images/da/scatter-dayofyear.bc171c189c9fd553.webp" width="50%" />
Lad os se, om der er en korrelation ved hjælp af funktionen `corr`:
@ -137,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()):
ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)
```
<img alt="Scatterplot af pris vs. dag i året" src="images/scatter-dayofyear-color.png" width="50%" />
<img alt="Scatterplot af pris vs. dag i året" src="../../../../translated_images/da/scatter-dayofyear-color.65790faefbb9d54f.webp" width="50%" />
Vores undersøgelse antyder, at sorten har større effekt på den samlede pris end den faktiske salgsdato. Vi kan se dette med et søjlediagram:
@ -145,7 +136,7 @@ Vores undersøgelse antyder, at sorten har større effekt på den samlede pris e
new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')
```
<img alt="Søjlediagram af pris vs. sort" src="images/price-by-variety.png" width="50%" />
<img alt="Søjlediagram af pris vs. sort" src="../../../../translated_images/da/price-by-variety.744a2f9925d9bcb4.webp" width="50%" />
Lad os fokusere for øjeblikket kun på én græskarsort, 'pie type', og se, hvilken effekt datoen har på prisen:
@ -153,7 +144,7 @@ Lad os fokusere for øjeblikket kun på én græskarsort, 'pie type', og se, hvi
pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']
pie_pumpkins.plot.scatter('DayOfYear','Price')
```
<img alt="Scatterplot af pris vs. dag i året" src="images/pie-pumpkins-scatter.png" width="50%" />
<img alt="Scatterplot af pris vs. dag i året" src="../../../../translated_images/da/pie-pumpkins-scatter.d14f9804a53f927e.webp" width="50%" />
Hvis vi nu beregner korrelationen mellem `Price` og `DayOfYear` ved hjælp af funktionen `corr`, får vi noget som `-0.27` - hvilket betyder, at det giver mening at træne en forudsigelsesmodel.
@ -227,7 +218,7 @@ plt.scatter(X_test,y_test)
plt.plot(X_test,pred)
```
<img alt="Lineær regression" src="images/linear-results.png" width="50%" />
<img alt="Lineær regression" src="../../../../translated_images/da/linear-results.f7c3552c85b0ed1c.webp" width="50%" />
## Polynomisk Regression
@ -256,7 +247,7 @@ Ved at bruge `PolynomialFeatures(2)` betyder det, at vi vil inkludere alle anden
Pipelines kan bruges på samme måde som det originale `LinearRegression`-objekt, dvs. vi kan `fit` pipelinen og derefter bruge `predict` til at få forudsigelsesresultater. Her er grafen, der viser testdata og tilnærmningskurven:
<img alt="Polynomisk regression" src="images/poly-results.png" width="50%" />
<img alt="Polynomisk regression" src="../../../../translated_images/da/poly-results.ee587348f0f1f60b.webp" width="50%" />
Ved at bruge polynomisk regression kan vi få en lidt lavere MSE og højere bestemmelseskoefficient, men ikke markant. Vi skal tage andre funktioner i betragtning!
@ -274,7 +265,7 @@ I en ideel verden ønsker vi at kunne forudsige priser for forskellige græskars
Her kan du se, hvordan gennemsnitsprisen afhænger af sorten:
<img alt="Gennemsnitspris efter sort" src="images/price-by-variety.png" width="50%" />
<img alt="Gennemsnitspris efter sort" src="../../../../translated_images/da/price-by-variety.744a2f9925d9bcb4.webp" width="50%" />
For at tage sorten i betragtning skal vi først konvertere den til numerisk form, eller **kode** den. Der er flere måder, vi kan gøre det på:

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# Opret en regressionsmodel
## Instruktioner

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# Logistisk regression til at forudsige kategorier
![Infografik om logistisk vs. lineær regression](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)

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# Gentagelse af noget Regression
## Instruktioner

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# Regressionsmodeller for maskinlæring
## Regionalt emne: Regressionsmodeller for græskarpriser i Nordamerika 🎃

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# Byg en webapp til at bruge en ML-model
I denne lektion vil du træne en ML-model på et datasæt, der er helt ude af denne verden: _UFO-observationer over det sidste århundrede_, hentet fra NUFORC's database.

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# Prøv en anden model
## Instruktioner

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# Byg en webapp til at bruge din ML-model
I denne del af pensum vil du blive introduceret til et anvendt ML-emne: hvordan du gemmer din Scikit-learn-model som en fil, der kan bruges til at lave forudsigelser i en webapplikation. Når modellen er gemt, lærer du, hvordan du bruger den i en webapp bygget i Flask. Først opretter du en model ved hjælp af nogle data, der handler om UFO-observationer! Derefter bygger du en webapp, der giver dig mulighed for at indtaste et antal sekunder sammen med en bredde- og længdegradsværdi for at forudsige, hvilket land der rapporterede at have set en UFO.

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# Introduktion til klassifikation
I disse fire lektioner vil du udforske et grundlæggende fokusområde inden for klassisk maskinlæring - _klassifikation_. Vi vil gennemgå brugen af forskellige klassifikationsalgoritmer med et datasæt om alle de fantastiske køkkener fra Asien og Indien. Håber du er sulten!

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# Udforsk klassifikationsmetoder
## Instruktioner

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# Klassifikatorer for køkkener 1
I denne lektion vil du bruge det datasæt, du gemte fra den sidste lektion, fyldt med balancerede og rene data om køkkener.

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# Undersøg løserne
## Instruktioner

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# Klassifikatorer for køkken 2
I denne anden lektion om klassifikation vil du udforske flere måder at klassificere numeriske data på. Du vil også lære om konsekvenserne ved at vælge én klassifikator frem for en anden.

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# Parameterleg
## Instruktioner

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# Byg en webapp til anbefaling af køkkener
I denne lektion vil du bygge en klassifikationsmodel ved hjælp af nogle af de teknikker, du har lært i tidligere lektioner, og med det lækre køkkendatasæt, der er blevet brugt gennem hele denne serie. Derudover vil du bygge en lille webapp til at bruge en gemt model, der udnytter Onnx's web-runtime.

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# Byg en anbefalingsmotor
## Instruktioner

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# Kom godt i gang med klassifikation
## Regionalt emne: Lækre asiatiske og indiske køkkener 🍜

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# Introduktion til clustering
Clustering er en type [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning), der antager, at et datasæt er ulabeleret, eller at dets input ikke er matchet med foruddefinerede output. Det bruger forskellige algoritmer til at sortere gennem ulabeleret data og levere grupperinger baseret på mønstre, det identificerer i dataene.

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# Undersøg andre visualiseringer for klyngedannelse
## Instruktioner

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# K-Means clustering
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)

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# Prøv forskellige klyngemetoder
## Instruktioner

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# Klyngemodeller til maskinlæring
Klyngedannelse er en maskinlæringsopgave, hvor man forsøger at finde objekter, der ligner hinanden, og gruppere dem i grupper kaldet klynger. Det, der adskiller klyngedannelse fra andre tilgange inden for maskinlæring, er, at processen sker automatisk. Faktisk kan man sige, at det er det modsatte af superviseret læring.

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# Introduktion til naturlig sprogbehandling
Denne lektion dækker en kort historie og vigtige begreber inden for *naturlig sprogbehandling*, et underfelt af *computational linguistics*.

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# Søg efter en bot
## Instruktioner

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# Almindelige opgaver og teknikker inden for naturlig sprogbehandling
For de fleste *naturlig sprogbehandling*-opgaver skal teksten, der skal behandles, opdeles, analyseres, og resultaterne gemmes eller krydsrefereres med regler og datasæt. Disse opgaver gør det muligt for programmøren at udlede _betydningen_ eller _intentionen_ eller blot _frekvensen_ af termer og ord i en tekst.

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# Få en bot til at svare tilbage
## Instruktioner

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# Oversættelse og sentimentanalyse med ML
I de tidligere lektioner lærte du, hvordan man bygger en grundlæggende bot ved hjælp af `TextBlob`, et bibliotek, der integrerer ML bag kulisserne for at udføre grundlæggende NLP-opgaver som udtrækning af navneordssætninger. En anden vigtig udfordring inden for computerlingvistik er præcis _oversættelse_ af en sætning fra et talesprog eller skriftsprog til et andet.

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# Poetisk frihed
## Instruktioner

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dette er en midlertidig pladsholder
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# Sentimentanalyse med hotelanmeldelser - bearbejdning af data
I denne sektion vil du bruge teknikkerne fra de tidligere lektioner til at lave en udforskende dataanalyse af et stort datasæt. Når du har fået en god forståelse af nytten af de forskellige kolonner, vil du lære:

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# NLTK
## Instruktioner

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dette er en midlertidig pladsholder
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# Sentimentanalyse med hotelanmeldelser
Nu hvor du har udforsket datasættet i detaljer, er det tid til at filtrere kolonnerne og derefter bruge NLP-teknikker på datasættet for at få nye indsigter om hotellerne.

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# Prøv et andet datasæt
## Instruktioner

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# Kom godt i gang med naturlig sprogbehandling
Naturlig sprogbehandling (NLP) er evnen for et computerprogram til at forstå menneskeligt sprog, som det tales og skrives – kaldet naturligt sprog. Det er en komponent af kunstig intelligens (AI). NLP har eksisteret i mere end 50 år og har rødder i lingvistik. Hele området er rettet mod at hjælpe maskiner med at forstå og bearbejde menneskeligt sprog. Dette kan derefter bruges til at udføre opgaver som stavekontrol eller maskinoversættelse. Det har en række praktiske anvendelser inden for flere områder, herunder medicinsk forskning, søgemaskiner og forretningsanalyse.

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Download hotelanmeldelsesdataene til denne mappe.
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# Introduktion til tidsserieforudsigelse
![Oversigt over tidsserier i en sketchnote](../../../../sketchnotes/ml-timeseries.png)

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# Visualiser nogle flere tidsserier
## Instruktioner

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# Tidsserieprognoser med ARIMA
I den forrige lektion lærte du lidt om tidsserieprognoser og indlæste et datasæt, der viser udsving i elektrisk belastning over en tidsperiode.

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# En ny ARIMA-model
## Instruktioner

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# Tidsserieforudsigelse med Support Vector Regressor
I den forrige lektion lærte du, hvordan man bruger ARIMA-modellen til at lave tidsserieforudsigelser. Nu skal du se på Support Vector Regressor-modellen, som er en regressionsmodel, der bruges til at forudsige kontinuerlige data.

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# En ny SVR-model
## Instruktioner [^1]

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# Introduktion til tidsserieforudsigelse
Hvad er tidsserieforudsigelse? Det handler om at forudsige fremtidige begivenheder ved at analysere tidligere tendenser.

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# Introduktion til Forstærkningslæring og Q-Learning
![Oversigt over forstærkning i maskinlæring i en sketchnote](../../../../sketchnotes/ml-reinforcement.png)

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# En Mere Realistisk Verden
I vores situation kunne Peter bevæge sig rundt næsten uden at blive træt eller sulten. I en mere realistisk verden skal han sætte sig ned og hvile fra tid til anden og også sørge for at spise. Lad os gøre vores verden mere realistisk ved at implementere følgende regler:

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# CartPole Skating
Problemet, vi har arbejdet med i den tidligere lektion, kan virke som et legetøjsproblem, der ikke rigtig har relevans for virkelige scenarier. Dette er dog ikke tilfældet, da mange virkelige problemer deler samme karakteristika – herunder at spille skak eller Go. De er ens, fordi vi også har et bræt med givne regler og en **diskret tilstand**.

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# Træn Mountain Car
[OpenAI Gym](http://gym.openai.com) er designet på en måde, hvor alle miljøer tilbyder den samme API - dvs. de samme metoder `reset`, `step` og `render`, samt de samme abstraktioner af **aktionsrum** og **observationsrum**. Derfor bør det være muligt at tilpasse de samme forstærkningslæringsalgoritmer til forskellige miljøer med minimale kodeændringer.

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# Introduktion til forstærkningslæring
Forstærkningslæring, RL, betragtes som en af de grundlæggende paradigmer inden for maskinlæring, ved siden af superviseret læring og usuperviseret læring. RL handler om beslutninger: at træffe de rigtige beslutninger eller i det mindste lære af dem.

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# Postscript: Maskinlæring i den virkelige verden
![Oversigt over maskinlæring i den virkelige verden i en sketchnote](../../../../sketchnotes/ml-realworld.png)

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# En ML Skattejagt
## Instruktioner

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# Postscript: Model Debugging i Maskinlæring ved hjælp af komponenter fra Responsible AI-dashboardet
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)

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# Udforsk Responsible AI (RAI) dashboard
## Instruktioner

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# Postscript: Virkelige anvendelser af klassisk maskinlæring
I denne del af pensum vil du blive introduceret til nogle virkelige anvendelser af klassisk maskinlæring. Vi har gennemsøgt internettet for at finde videnskabelige artikler og artikler om anvendelser, der har brugt disse strategier, og undgået neurale netværk, dyb læring og AI så meget som muligt. Lær om, hvordan maskinlæring bruges i forretningssystemer, økologiske anvendelser, finans, kunst og kultur og meget mere.

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# AGENTS.md
## Projektoversigt

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# Microsoft Open Source Adfærdskodeks
Dette projekt har vedtaget [Microsoft Open Source Adfærdskodeks](https://opensource.microsoft.com/codeofconduct/).

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# Bidrag
Dette projekt byder velkommen til bidrag og forslag. De fleste bidrag kræver, at du

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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 stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
### 🌐 Multisprog Support
### 🌐 Multi-sprog support
#### Understøttet via GitHub Action (Automatiseret & Altid Opdateret)
#### Understøttet via GitHub Action (Automatisk & altid opdateret)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
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[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](./README.md) | [Dutch](../nl/README.md) | [Estonian](../et/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)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-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)
> **Foretrækker du at klone lokalt?**
> Dette arkiv inkluderer over 50 sprogoversættelser, hvilket væsentligt øger downloadstørrelsen. For at klone uden oversættelser, brug sparse checkout:
> Dette repo indeholder 50+ sprogoversættelser, hvilket væsentligt øger downloadstørrelsen. For at klone uden oversættelser, brug sparse checkout:
> ```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'
> ```
> Dette giver dig alt, hvad du behøver for at fuldføre kurset med en meget hurtigere download.
> Dette giver dig alt hvad du behøver for at gennemføre kurset med en meget hurtigere download.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
#### Deltag i vores fællesskab
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Vi har en igangværende Discord lær-serie med AI, lær mere og deltag hos [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du får tips og tricks til brug af GitHub Copilot til Data Science.
Vi har en Discord-serie "lær med AI" i gang, lær mere og deltag hos [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du vil få tips og tricks til brugen af GitHub Copilot til data science.
![Learn with AI series](../../../../translated_images/da/3.9b58fd8d6c373c20.webp)
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# Maskinlæring for Begyndere - En Læreplan
# Machine Learning for Beginners - En læseplan
> 🌍 Rejs rundt i verden, mens vi udforsker Maskinlæring gennem verdens kulturer 🌍
> 🌍 Rejs rundt i verden, mens vi udforsker maskinlæring gennem verdens kulturer 🌍
Cloud Advocates hos Microsoft er glade for at tilbyde en 12-ugers, 26-lektioners læreplan om **Maskinlæring**. I denne læreplan vil du lære om det, der nogle gange kaldes **klassisk maskinlæring**, primært ved brug af Scikit-learn som bibliotek og uden at dække dyb læring, som behandles i vores [AI for Beginners' læreplan](https://aka.ms/ai4beginners). Kombiner disse lektioner med vores ['Data Science for Beginners' læreplan](https://aka.ms/ds4beginners) også!
Cloud Advocates hos Microsoft tilbyder en 12-ugers, 26-lektioners læseplan om **Maskinlæring**. I denne læseplan lærer du om det, der med et andet ord kaldes **klassisk maskinlæring**, primært ved brug af Scikit-learn som bibliotek og uden dyb læring, som dækkes i vores [AI for Beginners-læseplan](https://aka.ms/ai4beginners). Kombiner disse lektioner med vores ['Data Science for Beginners'-læseplan](https://aka.ms/ds4beginners) også!
Rejs med os rundt i verden, mens vi anvender disse klassiske teknikker på data fra mange verdensdele. Hver lektion indeholder quizzer før og efter lektionen, skriftlige instruktioner til at gennemføre lektionen, en løsning, en opgave og mere. Vores projektbaserede pædagogik giver dig mulighed for at lære ved at bygge, en gennemprøvet måde at få nye færdigheder til at "sidde fast".
Rejs med os rundt i verden, mens vi anvender disse klassiske teknikker på data fra mange områder. Hver lektion indeholder quizzer før og efter lektionen, skriftlige instruktioner til at fuldføre lektionen, en løsning, en opgave og mere. Vores projektbaserede metode giver dig mulighed for at lære mens du bygger, en bevist måde at få nye færdigheder til at 'sætte sig'.
**✍️ Hjertelig tak til vores forfattere** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu og Amy Boyd
**✍️ Hjertevarme tak til vores forfattere** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu og Amy Boyd
**🎨 Tak også til vores illustratorer** Tomomi Imura, Dasani Madipalli og Jen Looper
**🙏 Særlige tak 🙏 til vores Microsoft Student Ambassador-forfattere, anmeldere og indholdsbidragsydere**, især Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila og Snigdha Agarwal
**🙏 Særlig tak 🙏 til vores Microsoft Student Ambassador-forfattere, anmeldere og indholdsbidragydere**, især Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila og Snigdha Agarwal
**🤩 Ekstra tak til Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi og Vidushi Gupta for vores R-lektioner!**
# Kom godt i gang
# Kom i gang
Følg disse trin:
1. **Fork repository**: Klik på "Fork" knappen øverst til højre på denne side.
2. **Klon repository**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
1. **Fork Repoet**: Klik på "Fork" knappen øverst til højre på denne side.
2. **Klon Repoet**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
> [find alle yderligere ressourcer til dette kursus i vores Microsoft Learn-samling](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
> 🔧 **Brug for hjælp?** Tjek vores [Fejlfinding guide](TROUBLESHOOTING.md) for løsninger på almindelige problemer med installation, opsætning og afvikling af lektioner.
> 🔧 **Brug for hjælp?** Se vores [Fejlfinding-guide](TROUBLESHOOTING.md) for løsninger på almindelige problemer med installation, opsætning og kørsel af lektioner.
**[Studerende](https://aka.ms/student-page)**, for at bruge denne læreplan, forke hele repoet til din egen GitHub-konto og gennemfør øvelserne alene eller i en gruppe:
**[Studerende](https://aka.ms/student-page)**, for at bruge denne læseplan, fork det hele repo til din egen GitHub-konto og gennemfør øvelserne på egen hånd eller i gruppe:
- Start med en quiz før forelæsningen.
- Læs forelæsningen og gennemfør aktiviteterne, stop op og reflekter ved hver videnscheck.
- Prøv at skabe projekterne ved at forstå lektionerne fremfor blot at køre løsningskoden; dog er denne kode tilgængelig i `/solution`-mapperne i hver projektorienteret lektion.
- Tag quizzen efter forelæsningen.
- Gennemfør udfordringen.
- Gennemfør opgaven.
- Efter at have fuldført en lektion gruppe, besøg [Diskussionsforum](https://github.com/microsoft/ML-For-Beginners/discussions) og "lær højt" ved at udfylde den passende PAT-rubrik. En 'PAT' er et fremskridtsvurderingsværktøj, som er en rubrik du udfylder for at fremme din læring. Du kan også reagere på andre PAT’er, så vi kan lære sammen.
- Start med en quiz før lektionen.
- Læs lektionen og fuldfør aktiviteterne, pause og reflektere ved hver videnstest.
- Forsøg at skabe projekterne ved at forstå lektionerne i stedet for blot at køre løsningskoden; denne kode er dog tilgængelig i `/solution` mapperne i hver projektorienteret lektion.
- Tag quizzen efter lektionen.
- Fuldfør udfordringen.
- Fuldfør opgaven.
- Efter at have gennemført en lektiongruppe, besøg [Diskussionspanelet](https://github.com/microsoft/ML-For-Beginners/discussions) og "lær højt" ved at udfylde den relevante PAT-rubrik. En 'PAT' er et Progress Assessment Tool, som er en rubrik du udfylder for at fremme din læring. Du kan også reagere på andre PATs, så vi kan lære sammen.
> For yderligere studier anbefaler vi at følge disse [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) moduler og læringsstier.
> Til videre studier anbefaler vi at følge disse [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) moduler og læringsforløb.
**Lærere**, vi har [inkluderet nogle forslag](for-teachers.md) til, hvordan man bruger denne læreplan.
**Undervisere**, vi har [inkluderet nogle forslag](for-teachers.md) til, hvordan denne læseplan kan anvendes.
---
## Video-gennemgange
Nogle af lektionerne findes som korte videoer. Du kan finde dem alle integreret i lektionerne eller på [ML for Beginners playliste på Microsoft Developers YouTube kanal](https://aka.ms/ml-beginners-videos) ved at klikke på billedet nedenfor.
Nogle af lektionerne findes som korte videoer. Du kan finde dem alle integreret i lektionerne eller på [ML for Beginners-afspilningslisten på Microsoft Developer YouTube-kanalen](https://aka.ms/ml-beginners-videos) ved at klikke på billedet nedenfor.
[![ML for beginners banner](../../../../translated_images/da/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
[![ML for beginners banner](../../translated_images/da/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
---
@ -100,79 +91,79 @@ Nogle af lektionerne findes som korte videoer. Du kan finde dem alle integreret
**Gif af** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
> 🎥 Klik på billedet ovenfor for en video om projektet og folkene, der skabte det!
> 🎥 Klik på billedet ovenfor for en video om projektet og folkene bag!
---
## Pædagogik
Vi har valgt to pædagogiske principper under opbygningen af denne læreplan: at sikre, at den er praktisk **projektbaseret** og at den indeholder **hyppige quizzer**. Derudover har denne læreplan et fælles **tema** for at skabe sammenhæng.
Vi har valgt to pædagogiske principper under udviklingen af denne læseplan: sikring af at den er praktisk **projektbaseret** og at den inkluderer **hyppige quizzer**. Derudover har læseplanen et fælles **tema** for at give den sammenhæng.
Ved at sikre at indholdet stemmer overens med projekterne, gøres processen mere engagerende for eleverne, og fastholdelse af begreber vil blive forstærket. Derudover sætter en quiz med lav indsats før en lektion elevens intention mod at lære et emne, mens en anden quiz efter lektionen sikrer øget fastholdelse. Denne læreplan er designet til at være fleksibel og sjov og kan tages helt eller delvist. Projekterne starter små og bliver mere komplekse i slutningen af det 12-ugers forløb. Denne læreplan inkluderer også et efterord om virkelige anvendelser af ML, som kan bruges som ekstra kredit eller som grundlag for diskussion.
Ved at sikre, at indholdet stemmer overens med projekterne, gøres processen mere engagerende for eleverne, og fastholdelsen af koncepter styrkes. En quiz med lav indsats før en klasse sætter elevens intention om at lære et emne, mens en anden quiz efter klassen sikrer yderligere fastholdelse. Denne læseplan er designet til at være fleksibel og sjov og kan tages helt eller delvist. Projekterne starter småt og bliver mere komplekse mod slutningen af 12-ugers perioden. Denne læseplan inkluderer også et efterskrift om maskinlæringens anvendelser i den virkelige verden, som kan bruges som ekstrakredit eller som grundlag for diskussion.
> Find vores [Adfærdskodeks](CODE_OF_CONDUCT.md), [Bidrag](CONTRIBUTING.md), [Oversættelse](TRANSLATIONS.md), og [Fejlfinding](TROUBLESHOOTING.md) retningslinjer. Vi byder dit konstruktive feedback velkommen!
> Find vores [Adfærdsregler](CODE_OF_CONDUCT.md), [Bidrag](CONTRIBUTING.md), [Oversættelse](TRANSLATIONS.md) og [Fejlfinding](TROUBLESHOOTING.md) retningslinjer. Vi værdsætter din konstruktive feedback!
## Hver lektion inkluderer
## Hver lektion indeholder
- valgfri sketchnote
- valgfrit sketchnote
- valgfri supplerende video
- video-gennemgang (kun nogle lektioner)
- [varme-op quiz før forelæsningen](https://ff-quizzes.netlify.app/en/ml/)
- [warmup-quiz før lektionen](https://ff-quizzes.netlify.app/en/ml/)
- skriftlig lektion
- for projektbaserede lektioner, trin-for-trin vejledninger til hvordan man bygger projektet
- for projektbaserede lektioner: trin-for-trin vejledning til at bygge projektet
- videnscheck
- en udfordring
- supplerende læsning
- opgave
- [quiz efter forelæsningen](https://ff-quizzes.netlify.app/en/ml/)
> **Et notat om sprog**: Disse lektioner er primært skrevet i Python, men mange er også tilgængelige i R. For at gennemføre en R-lektion, gå til `/solution` mappen og find R-lektionerne. De inkluderer en .rmd-udvidelse, som repræsenterer en **R Markdown** fil, der kan defineres som en indlejring af `kodeblokke` (af R eller andre sprog) og en `YAML header` (der guider, hvordan output som PDF skal formateres) i et `Markdown dokument`. Som sådan fungerer det som en eksemplarisk forfatterramme for datalogi, da det giver mulighed for at kombinere din kode, dens output og dine tanker ved at skrive dem ned i Markdown. Desuden kan R Markdown-dokumenter genereres til outputformater såsom PDF, HTML eller Word.
> **En note om quizzer**: Alle quizzer findes i [Quiz App-mappen](../../quiz-app), med i alt 52 quizzer med tre spørgsmål hver. De er linket fra lektionerne, men quiz-appen kan køres lokalt; følg vejledningen i `quiz-app`-mappen for at hoste lokalt eller deploye til Azure.
| Lektion Nummer | Emne | Lektionens Kategori | Læringsmål | Linket Lektion | Forfatter |
| :------------: | :----------------------------------------------------------: | :-----------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------: |
| 01 | Introduktion til maskinlæring | [Introduktion](1-Introduction/README.md) | Lær de grundlæggende begreber bag maskinlæring | [Lektion](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | Maskinlæringens historie | [Introduktion](1-Introduction/README.md) | Lær historien bag dette felt | [Lektion](1-Introduction/2-history-of-ML/README.md) | Jen og Amy |
| 03 | Retfærdighed og maskinlæring | [Introduktion](1-Introduction/README.md) | Hvilke vigtige filosofiske spørgsmål omkring retfærdighed bør elever overveje, når de bygger og anvender ML-modeller? | [Lektion](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Teknikker til maskinlæring | [Introduktion](1-Introduction/README.md) | Hvilke teknikker bruger ML-forskere til at bygge ML-modeller? | [Lektion](1-Introduction/4-techniques-of-ML/README.md) | Chris og Jen |
| 05 | Introduktion til regression | [Regression](2-Regression/README.md) | Kom i gang med Python og Scikit-learn til regressionsmodeller | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau |
| 06 | Nordamerikanske græskarpriser 🎃 | [Regression](2-Regression/README.md) | Visualiser og rens data som forberedelse til ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau |
| 07 | Nordamerikanske græskarpriser 🎃 | [Regression](2-Regression/README.md) | Byg lineære og polynomielle regressionsmodeller | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen og Dmitry • Eric Wanjau |
| 08 | Nordamerikanske græskarpriser 🎃 | [Regression](2-Regression/README.md) | Byg en logistisk regressionsmodel | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau |
| 09 | En Web App 🔌 | [Web App](3-Web-App/README.md) | Byg en webapp til at bruge din trænede model | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduktion til klassifikation | [Classification](4-Classification/README.md) | Rens, forbered og visualiser dine data; introduktion til klassifikation | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen og Cassie • Eric Wanjau |
| 11 | Lækre asiatiske og indiske køkkener 🍜 | [Classification](4-Classification/README.md) | Introduktion til klassifikatorer | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen og Cassie • Eric Wanjau |
| 12 | Lækre asiatiske og indiske køkkener 🍜 | [Classification](4-Classification/README.md) | Flere klassifikatorer | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen og Cassie • Eric Wanjau |
| 13 | Lækre asiatiske og indiske køkkener 🍜 | [Classification](4-Classification/README.md) | Byg en anbefalings-webapp ved hjælp af din model | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduktion til klyngedannelse | [Clustering](5-Clustering/README.md) | Rens, forbered og visualiser dine data; introduktion til klyngedannelse | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau |
| 15 | Udforskning af nigerianske musiksmag 🎧 | [Clustering](5-Clustering/README.md) | Udforsk K-Means klyngemetoden | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau |
| 16 | Introduktion til naturlig sprogbehandling ☕️ | [Natural language processing](6-NLP/README.md) | Lær det grundlæggende om NLP ved at bygge en simpel bot | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Almindelige NLP-opgaver ☕️ | [Natural language processing](6-NLP/README.md) | Forstå almindelige opgaver, som kræves ved håndtering af sprogstrukturer | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Oversættelse og sentimentanalyse ♥️ | [Natural language processing](6-NLP/README.md) | Oversættelse og sentimentanalyse med Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantiske hoteller i Europa ♥️ | [Natural language processing](6-NLP/README.md) | Sentimentanalyse med hotelanmeldelser 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantiske hoteller i Europa ♥️ | [Natural language processing](6-NLP/README.md) | Sentimentanalyse med hotelanmeldelser 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduktion til tidsseriefremskrivning | [Time series](7-TimeSeries/README.md) | Introduktion til tidsseriefremskrivning | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ Verdens strømforbrug ⚡️ - tidsseriefremskrivning med ARIMA | [Time series](7-TimeSeries/README.md) | Tidsseriefremskrivning med ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ Verdens strømforbrug ⚡️ - tidsseriefremskrivning med SVR | [Time series](7-TimeSeries/README.md) | Tidsseriefremskrivning med Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Introduktion til forstærkningslæring | [Reinforcement learning](8-Reinforcement/README.md) | Introduktion til forstærkningslæring med Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Hjælp Peter med at undgå ulven! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Forstærkningslæring i Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Efterskrift | Reelle ML-scenarier og -anvendelser | [ML in the Wild](9-Real-World/README.md) | Interessante og oplysende virkelige anvendelser af klassisk ML | [Lektion](9-Real-World/1-Applications/README.md) | Team |
| Efterskrift | Fejlfinding af ML-modeller med RAI dashboard | [ML in the Wild](9-Real-World/README.md) | Fejlfinding af ML-modeller ved hjælp af Responsible AI-dashboard komponenter | [Lektion](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu |
> [find alle yderligere ressourcer til dette kursus i vores Microsoft Learn-samling](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
- [quiz efter lektionen](https://ff-quizzes.netlify.app/en/ml/)
> **En note om sprog**: Disse lektioner er primært skrevet i Python, men mange findes også i R. For at gennemføre en R-lektion, gå til `/solution` mappen og se efter R-lektioner. De har en .rmd extension, som repræsenterer en **R Markdown**-fil, der enkelt kan defineres som en sammenstilling af `kodeblokke` (fra R eller andre sprog) og en `YAML-header` (som styrer formateringen af output som PDF) i et `Markdown-dokument`. Dermed fungerer det som en fremragende forfatter-ramme for data science, da det lader dig kombinere din kode, dens output og dine tanker ved at skrive dem ned i Markdown. Desuden kan R Markdown-dokumenter gengives til outputformater som PDF, HTML eller Word.
> **En note om quizzer**: Alle quizzer findes i [Quiz App mappen](../../quiz-app), i alt 52 quizzer med tre spørgsmål hver. De er linket fra lektionerne, men quiz-app'en kan køres lokalt; følg instruktionerne i `quiz-app` mappen for at hoste lokalt eller deploye til Azure.
| Lektion Nummer | Emne | Lektion Gruppe | Læringsmål | Linket Lektion | Forfatter |
| :------------: | :------------------------------------------------------------: | :------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------: |
| 01 | Introduktion til maskinlæring | [Introduktion](1-Introduction/README.md) | Lær de grundlæggende begreber bag maskinlæring | [Lektion](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | Historien om maskinlæring | [Introduktion](1-Introduction/README.md) | Lær historien bag dette felt | [Lektion](1-Introduction/2-history-of-ML/README.md) | Jen og Amy |
| 03 | Retfærdighed og maskinlæring | [Introduktion](1-Introduction/README.md) | Hvad er de vigtige filosofiske spørgsmål omkring retfærdighed, som elever bør overveje, når de bygger og anvender ML-modeller? | [Lektion](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Teknikker til maskinlæring | [Introduktion](1-Introduction/README.md) | Hvilke teknikker bruger ML-forskere til at bygge ML-modeller? | [Lektion](1-Introduction/4-techniques-of-ML/README.md) | Chris og Jen |
| 05 | Introduktion til regression | [Regression](2-Regression/README.md) | Kom i gang med Python og Scikit-learn til regressionsmodeller | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau |
| 06 | Nordamerikanske græskarpriser 🎃 | [Regression](2-Regression/README.md) | Visualisere og rense data som forberedelse til ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau |
| 07 | Nordamerikanske græskarpriser 🎃 | [Regression](2-Regression/README.md) | Byg lineære og polynomiske regressionsmodeller | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen og Dmitry • Eric Wanjau |
| 08 | Nordamerikanske græskarpriser 🎃 | [Regression](2-Regression/README.md) | Byg en logistisk regressionsmodel | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau |
| 09 | En webapp 🔌 | [Web App](3-Web-App/README.md) | Byg en webapp til at bruge din trænede model | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduktion til klassifikation | [Classification](4-Classification/README.md) | Rens, forbered og visualiser dine data; introduktion til klassifikation | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen og Cassie • Eric Wanjau |
| 11 | Lækre asiatiske og indiske retter 🍜 | [Classification](4-Classification/README.md) | Introduktion til klassifikatorer | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen og Cassie • Eric Wanjau |
| 12 | Lækre asiatiske og indiske retter 🍜 | [Classification](4-Classification/README.md) | Flere klassifikatorer | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen og Cassie • Eric Wanjau |
| 13 | Lækre asiatiske og indiske retter 🍜 | [Classification](4-Classification/README.md) | Byg en anbefalings-webapp ved hjælp af din model | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduktion til clustering | [Clustering](5-Clustering/README.md) | Rens, forbered og visualiser dine data; introduktion til clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau |
| 15 | Udforskning af nigerianske musiksmag 🎧 | [Clustering](5-Clustering/README.md) | Udforsk K-Means clustering metoden | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau |
| 16 | Introduktion til naturlig sprogbehandling ☕️ | [Natural language processing](6-NLP/README.md) | Lær det grundlæggende om NLP ved at bygge en simpel bot | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Almindelige NLP opgaver ☕️ | [Natural language processing](6-NLP/README.md) | Fordyb din NLP-viden ved at forstå almindelige opgaver, der kræves ved håndtering af sproglige strukturer | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Oversættelse og sentimentanalyse ♥️ | [Natural language processing](6-NLP/README.md) | Oversættelse og sentimentanalyse med Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantiske hoteller i Europa ♥️ | [Natural language processing](6-NLP/README.md) | Sentimentanalyse med hotelanmeldelser 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantiske hoteller i Europa ♥️ | [Natural language processing](6-NLP/README.md) | Sentimentanalyse med hotelanmeldelser 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduktion til tidsserieprognose | [Time series](7-TimeSeries/README.md) | Introduktion til tidsserieprognose | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ Verdens strømforbrug ⚡️ - tidsserieprognose med ARIMA | [Time series](7-TimeSeries/README.md) | Tidsserieprognose med ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ Verdens strømforbrug ⚡️ - tidsserieprognose med SVR | [Time series](7-TimeSeries/README.md) | Tidsserieprognose med Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Introduktion til reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduktion til reinforcement learning med Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Hjælp Peter med at undgå ulven! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Efterskrift | Virkelige ML scenarier og anvendelser | [ML in the Wild](9-Real-World/README.md) | Interessante og afslørende virkelige anvendelser af klassisk ML | [Lektion](9-Real-World/1-Applications/README.md) | Team |
| Efterskrift | Modelafhjælpning i ML ved brug af RAI dashboard | [ML in the Wild](9-Real-World/README.md) | Modelafhjælpning i maskinlæring ved brug af Responsible AI dashboard komponenter | [Lektion](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu |
> [find alle yderligere ressourcer til dette kursus i vores Microsoft Learn samling](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
## Offline adgang
Du kan køre denne dokumentation offline ved at bruge [Docsify](https://docsify.js.org/#/). Fork dette repo, [installer Docsify](https://docsify.js.org/#/quickstart) på din lokale maskine, og derefter i root-mappen af dette repo, skriv `docsify serve`. Hjemmesiden vil blive serveret på port 3000 på din localhost: `localhost:3000`.
Du kan køre denne dokumentation offline ved hjælp af [Docsify](https://docsify.js.org/#/). Fork dette repo, [installer Docsify](https://docsify.js.org/#/quickstart) på din lokale maskine, og skriv derefter i rodmappen af dette repo `docsify serve`. Websitet vil blive servet på port 3000 på din localhost: `localhost:3000`.
## PDF'er
Find et pdf-udgave af pensum med links [her](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
Find en pdf af pensum med links [her](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
## 🎒 Andre kurser
## 🎒 Andre kurser
Vores team producerer andre kurser! Se her:
Vores team producerer andre kurser! Tjek dem ud:
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
@ -189,44 +180,44 @@ Vores team producerer andre kurser! Se her:
---
### Generativ AI-serie
[![Generative AI for Beginners](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)
[![Generative AI (.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)
[![Generative AI (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)
[![Generative AI (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)
### Generative AI Serie
[![Generativ AI for Begyndere](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)
[![Generativ AI (.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)
[![Generativ AI (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)
[![Generativ AI (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)
---
### Kerne Læring
[![ML for Beginners](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)
[![Data Science for Beginners](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)
[![AI for Beginners](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)
[![Cybersecurity for Beginners](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)
[![Web Dev for Beginners](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)
[![IoT for Beginners](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 Development for Beginners](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)
[![ML for Begyndere](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)
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[![AI for Begyndere](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)
[![Cybersikkerhed for Begyndere](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)
[![Webudvikling for Begyndere](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)
[![IoT for Begyndere](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-udvikling for Begyndere](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)
---
### Copilot Serie
[![Copilot for AI Paired Programming](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 for AI Parprogrammering](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 for C#/.NET](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)
[![Copilot Adventure](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)
[![Copilot Eventyr](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 -->
## Få Hjælp
## Få hjælp
Hvis du sidder fast eller har spørgsmål om at bygge AI-apps. Deltag i samtaler med andre lærende og erfarne udviklere om MCP. Det er et støttende fællesskab, hvor spørgsmål er velkomne, og viden deles frit.
Hvis du sidder fast eller har spørgsmål om at bygge AI-apps. Deltag med andre lærende og erfarne udviklere i diskussioner om MCP. Det er et støttende fællesskab, hvor spørgsmål er velkomne, og viden deles frit.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Hvis du har produktfeedback eller fejl under udvikling, besøg:
Hvis du har feedback på produktet eller fejl under udviklingen, besøg:
[![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 -->
**Ansvarsfraskrivelse**:
Dette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det oprindelige dokument på dets oprindelige sprog bør anses som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi kan ikke drages til ansvar for misforståelser eller fejltolkninger, der måtte opstå ved brug af denne oversættelse.
**Ansvarsfraskrivelse**:
Dette dokument er oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, bedes du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det oprindelige dokument på dets modersmål bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.
<!-- CO-OP TRANSLATOR DISCLAIMER END -->

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## Sikkerhed
Microsoft tager sikkerheden af vores softwareprodukter og -tjenester alvorligt, hvilket inkluderer alle kildekoderepositorier, der administreres gennem vores GitHub-organisationer, som inkluderer [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) og [vores GitHub-organisationer](https://opensource.microsoft.com/).

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# Support
## Sådan indsender du problemer og får hjælp

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# Fejlfindingsguide
Denne guide hjælper dig med at løse almindelige problemer, når du arbejder med Machine Learning for Beginners-kurset. Hvis du ikke finder en løsning her, kan du tjekke vores [Discord-diskussioner](https://aka.ms/foundry/discord) eller [oprette en sag](https://github.com/microsoft/ML-For-Beginners/issues).

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- Introduktion
- [Introduktion til Maskinlæring](../1-Introduction/1-intro-to-ML/README.md)
- [Historien om Maskinlæring](../1-Introduction/2-history-of-ML/README.md)

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## For undervisere
Vil du gerne bruge dette pensum i din undervisning? Du er meget velkommen!

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# Quizzer
Disse quizzer er før- og efterforelæsningsquizzer for ML-kurset på https://aka.ms/ml-beginners

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Rettigheder, så licenserer du den resulterende database under de samme betingelser som denne offentlige licens;
c. du må ikke tilbyde eller pålægge yderligere eller forskellige vilkår eller betingelser for, eller anvende Effektive Teknologiske Foranstaltninger på, den licenserede database, der begrænser udøvelsen af de rettigheder, der er givet under denne offentlige licens; og

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Alle sketchnotes fra pensum kan downloades her.
🖨 For udskrivning i høj opløsning er TIFF-versionerne tilgængelige på [dette repo](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff).

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