Aan de slag met Data Science Workspace for Data Scientists
Meer weten over Data Science Workspace in Adobe Experience Platform? Deze playlist wordt ontworpen voor gegevenswetenschappers die willen leren hoe te om Notities te gebruiken JupyterLab om inzichten en vraaggegevens af te leiden, profiel-toegelaten datasets tot stand te brengen, geautomatiseerde machine het leren modellen te publiceren, en machine-geleerde inzichten aan zowel Adobe als niet-Adobe toepassingen te activeren.
Overzicht van Data Science Workspace
De visie van machine het leren op Adobe Experience Platform is gegevenswetenschap te democratiseren door de domeindeskundigheid van de producten van de Adobe, klanten, en partners te gebruiken om een ecosysteem van intelligente diensten tot stand te brengen om de volgende generatie van klantenervaringen te drijven. Data Science Workspace maakt het gemakkelijk om toegang te krijgen tot omni-channel gegevens, modellen te bouwen, modellen te exploiteren met een éénklikimplementatie, en modelinzichten te verbruiken door hen via klantenprofielen in real time te delen. In deze video wordt een overzicht gegeven van wat Data Science Workspace is en van de waarde die deze biedt aan bedrijven.
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Overzicht van Workspace-architectuur voor gegevenswetenschappen
Deze video beschrijft de overkoepelende architectuur en illustreert de primaire componenten van Data Science Workspace in Adobe Experience Platform.
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Het cursusschema en de gegevensset maken
Leer hoe te om de de cursusdataset en het schema tot stand te brengen van de Wetenschap van Gegevens die in de rest van de cursus worden gebruikt.
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Gegevens laden in JupyterLab-laptops
In deze video ziet u hoe u een JupyterLab-laptop maakt en gegevens uit Adobe Experience Platform laadt. Het toont ook hoe u de prestaties van uw laptop kunt verbeteren wanneer u met grote hoeveelheden gegevens werkt.
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Gegevens opvragen en ontdekken in Data Science Workspace
Met Adobe Experience Platform kunt u SQL (Structured Query Language) gebruiken in Data Science Workspace door Query Service in JupyterLab te integreren als een standaardfunctie.
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Analyse van verkennende gegevens in Data Science Workspace
De verkennende zelfstudie van de Analyse van Gegevens (EDA) wordt ontworpen om u bij het ontdekken van patronen in gegevens te helpen, gegevensgezondheid te controleren, en het samenvatten van de relevante gegevens voor voorspellende modellen.
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Overzicht van recept, modellen en services
Meer informatie over recepten, modellen en services in Adobe Experience Platform Data Science Workspace.
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Modelprestaties analyseren
In deze video ziet u enkele van de verschillende methoden die worden gebruikt om de prestaties van een model te analyseren, zoals een verwarringsmatrix, nauwkeurigheid, recall en precisie.
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Een model maken met de sjabloon voor het samenstellen van het recept
In deze video ziet u hoe u het model voor het maken van het recept kunt gebruiken in de JupyterLab-startprogramma om een model voor eigenschappen op te leiden en te scoren en een recept te maken.
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Een getraind model maken en publiceren
Leer hoe u een model kunt maken, trainen, evalueren en publiceren met behulp van een recept dat is gemaakt met de JupyterLab-laptop voor het maken van recept.
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Geautomatiseerde training en scoring voor een service plannen
Leer hoe u automatische training en scoring instelt voor een service in Data Science Workspace.
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Uitvoer van machinaal leren gebruiken in segmentatie
Leer hoe de modeloutput van de Werkruimte van de Wetenschap van Gegevens in het Profiel en de segmentatie van de Klant in real time kan worden gebruikt.
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