Kom igång med Data Science Workspace for Data Scientists
Läs om Data Science Workspace i Adobe Experience Platform. Spellistan är utformad för datavetare som vill lära sig att använda JupyterLab-anteckningsböcker för att få insikter och frågedata, skapa profilaktiverade datauppsättningar, publicera automatiska maskininlärningsmodeller och aktivera maskininlärda insikter för både Adobe och andra program.
Data Science Workspace - översikt
Visionen med maskininlärning på Adobe Experience Platform är att demokratisera datavetenskapen genom att använda expertkunskapen hos Adobe-produkter, kunder och partners för att skapa ett ekosystem av intelligenta tjänster för att driva nästa generation av kundupplevelser. Data Science Workspace gör det enkelt att få tillgång till flerkanaliga data, bygga modeller, driftsätta modeller med en enda klicksdistribution och konsumera modellinsikter genom att dela dem via kundprofiler i realtid. Den här videon ger en översikt över vad Data Science Workspace är och det värde det ger företag.
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Data Science Workspace Architecture overview
Den här videon beskriver den övergripande arkitekturen och illustrerar huvudkomponenterna i Data Science Workspace i Adobe Experience Platform.
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Skapa kursschema och datauppsättning
Lär dig hur du skapar data Science Workspace-kursdatauppsättningen och -schemat som används i resten av kursen.
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Läs in data i JupyterLab-anteckningsböcker
I den här videon visas hur du skapar en JupyterLab-anteckningsbok och läser in data från Adobe Experience Platform. Den visar också hur du kan förbättra prestandan för din bärbara dator när du arbetar med stora mängder data.
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Fråga och hitta data i Data Science Workspace
Med Adobe Experience Platform kan du använda SQL (Structured Query Language) i Data Science Workspace genom att integrera Query Service i JupyterLab som standardfunktion.
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Analys av data i datavetenskap i Workspace
EDA-självstudiekursen (Exploratory Data Analysis) är utformad för att hjälpa dig att upptäcka mönster i data, kontrollera datasäktheten och sammanfatta relevanta data för prediktiva modeller.
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Recept, modeller och tjänster - översikt
Läs mer om recept, modeller och tjänster i Adobe Experience Platform Data Science Workspace.
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Analysera modellprestanda
Lär dig mer om några av de olika metoder som används för att analysera en modells prestanda, till exempel en sammanväxlingsmatris, noggrannhet, återkallelse och precision.
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Bygg en modell med mallen recept builder
I den här videon visas exempel med hjälp av mallen recept builder i JupyterLab-startprogrammet för att träna och poängsätta en benägenhetsmodell och skapa ett recept.
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Skapa och publicera en tränad modell
Lär dig hur du skapar, utbildar, utvärderar och publicerar en modell med ett recept som skapats med JupyterLab Recipe Builder-anteckningsboken.
184
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Boka in automatiserad utbildning och poängsättning för en tjänst
Lär dig hur du skapar automatiserad utbildning och poängsättning för en tjänst i Data Science Workspace.
172
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Använd maskininlärningsresultat vid segmentering
Läs om hur data Science Workspace-modellutdata kan användas i kundprofiler och segmentering i realtid.
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