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Cisco utbildning

Insoft Services är en av få utbildningsleverantörer inom EMEAR som erbjuder hela utbudet av Cisco-certifiering och specialiserad teknikutbildning.

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Cisco-certifieringar

Upplev en blandad inlärningsmetod som kombinerar det bästa av instruktörsledd utbildning och e-lärande i egen takt för att hjälpa dig att förbereda dig för ditt certifieringsprov.

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Cisco Learning Credits

Cisco Learning Credits (CLC) är förbetalda utbildningskuponger som löses in direkt med Cisco och som gör det enklare att planera för din framgång när du köper Ciscos produkter och tjänster.

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Cisco Fortbildning

Ciscos fortbildningsprogram erbjuder alla aktiva certifikatinnehavare flexibla alternativ för att omcertifiera genom att slutföra en mängd olika kvalificerade utbildningsartiklar.

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Cisco Digital Learning

Certifierade medarbetare är VÄRDERADE tillgångar. Utforska Ciscos officiella digitala utbildningsbibliotek för att utbilda dig själv genom inspelade sessioner.

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Partner för affärsaktivering

Cisco Business Enablement Partner Program fokuserar på att vässa affärskunskaperna hos Cisco Channel Partners och kunder.

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Cisco Kurskatalog

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Fortinet-certifieringar

Fortinet Network Security Expert (NSE) -programmet är ett utbildnings- och certifieringsprogram på åtta nivåer för att lära ingenjörer om deras nätverkssäkerhet för Fortinet FW-färdigheter och erfarenheter.

Tekniska utbildningar

Tekniska utbildningar

Insoft är erkänt som Fortinet Authorized Training Center på utvalda platser i EMEA.

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Fortinet Kurskatalog

Utforska ett brett utbud av Fortinet-scheman i olika länder samt onlinekurser.

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ATC-status

Kolla in vår ATC-status i utvalda länder i Europa.

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Fortinet Professionella tjänster

Globalt erkända team av certifierade experter hjälper dig att göra en smidigare övergång med våra fördefinierade konsult-, installations- och migreringspaket för ett brett utbud av Fortinet-produkter.

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Microsoft-utbildning

Insoft Services tillhandahåller Microsoft-utbildning i EMEAR. Vi erbjuder Microsofts tekniska utbildnings- och certifieringskurser som leds av instruktörer i världsklass.

Tekniska utbildningar

Extreme-utbildning

Lär dig exceptionella kunskaper och färdigheter i Extreme Networks.

Technische Kurse

Tekniske-certifieringar

Vi tillhandahåller omfattande läroplan för tekniska kompetensfärdigheter på certifieringsprestationen.

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Extreme Kurskatalog

Hier finden Sie alle Extreme Networks online und den von Lehrern geleiteten Kalender für den Klassenraum.

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ATP-ackreditering

Som auktoriserad utbildningspartner (ATP) säkerställer Insoft Services att du får de högsta tillgängliga utbildningsstandarderna.

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Konsultpaket

Vi erbjuder innovativt och avancerat stöd för att designa, implementera och optimera IT-lösningar.Vår kundbas inkluderar några av de största telekombolagen globalt.

Lösningar och tjänster

Globalt erkända team av certifierade experter hjälper dig att göra en smidigare övergång med våra fördefinierade konsult-, installations- och migreringspaket för ett brett utbud av Fortinet-produkter.

Om oss

Insoft Tillhandahåller auktoriserade utbildnings- och konsulttjänster för utvalda IP-leverantörer.Lär dig hur vi revolutionerar branschen.

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    • Applied Unsupervised Learning with R

    Enroll Now
    Duration
    2 Dagar
    Delivery
    (Online och på plats)
    Price
    Pris på begäran
    Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions. This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the course also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this course, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.  

    After completing this course, you will be able to:

    • Implement clustering methods such as agglomerative, and divisive
    • Write code in R to analyze market segmentation and consumer behaviour
    • Estimate distribution and probabilities of different outcomes
    • Implement dimension reduction using principal component analysis
    • Apply anomaly detection methods to identify fraud
    • Design algorithms with R and learn how to edit or improve code

    Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning.

    Although the course is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

     

    Hardware:

    For the optimal student experience, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 4 GB RAM
    • Storage: 5 GB available space
    • An internet connection

     

    Software:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X
    • R (3.0.0 or more recent, available for free at https://cran.r-project.org/)
    Overview
    Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions. This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the course also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this course, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.  

    After completing this course, you will be able to:

    • Implement clustering methods such as agglomerative, and divisive
    • Write code in R to analyze market segmentation and consumer behaviour
    • Estimate distribution and probabilities of different outcomes
    • Implement dimension reduction using principal component analysis
    • Apply anomaly detection methods to identify fraud
    • Design algorithms with R and learn how to edit or improve code

    Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning.

    Although the course is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

     

    Hardware:

    For the optimal student experience, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 4 GB RAM
    • Storage: 5 GB available space
    • An internet connection

     

    Software:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X
    • R (3.0.0 or more recent, available for free at https://cran.r-project.org/)
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