Cisco træning

Insoft Services er en af de få uddannelsesudbydere i EMEAR, der tilbyder hele spektret af Cisco-certificering og specialiseret teknologiuddannelse.

Lær hvordan

Cisco-certificeringer

Oplev en blandet læringsmetode, der kombinerer det bedste fra instruktørstyret træning og e-læring i eget tempo for at hjælpe dig med at forberede dig til din certificeringseksamen.

Lær hvordan

Cisco Learning Credits

Cisco Learning Credits (CLCs) er forudbetalte træningskuponer, der indløses direkte med Cisco, og som gør det nemmere at planlægge din succes, når du køber Cisco-produkter og -tjenester.

Lær hvordan

Cisco Efteruddannelse

Cisco Continuing Education Program tilbyder alle aktive certificeringsindehavere fleksible muligheder for at gencertificere ved at gennemføre en række kvalificerede træningselementer.

Lær hvordan

Cisco Digital Learning

Certificerede medarbejdere er VÆRDSATTE aktiver. Udforsk Ciscos officielle digitale læringsbibliotek for at uddanne dig selv gennem optagede sessioner.

Lær hvordan

Cisco Business Enablement

Cisco Business Enablement Partner Program fokuserer på at skærpe Cisco Channel Partners og kunders forretningsmæssige færdigheder.

Lær hvordan

Cisco kursuskatalog

Lær hvordan

Fortinet-certificeringer

Fortinet Network Security Expert (NSE) -programmet er et otte-niveau uddannelses- og certificeringsprogram for at undervise ingeniører i deres netværkssikkerhed for Fortinet FW-færdigheder og erfaring.

Lær hvordan

Fortinet træning

Insoft er anerkendt som Autoriseret Fortinet Training Center på udvalgte steder på tværs af EMEA.

Tekniske kurser

Fortinet kursuskatalog

Udforsk hele Fortinet-træningskataloget. Programmet omfatter en bred vifte af selvstændige og instruktørledede kurser.

Lær hvordan

ATC-status

Tjek vores ATC-status på tværs af udvalgte lande i Europa.

Lær hvordan

Fortinet Professionelle Services

Globalt anerkendte team af certificerede eksperter hjælper dig med at gøre en mere jævn overgang med vores foruddefinerede konsulent-, installations- og migreringspakker til en lang række Fortinet-produkter.

Lær hvordan

Microsoft træning

Insoft Services tilbyder Microsoft-undervisning i EMEAR. Vi tilbyder Microsoft tekniske kurser og certificeringskurser, der ledes af instruktører i verdensklasse.

Tekniske kurser

Extreme træning

Find all the Extreme Networks online and instructor led class room based calendar here.

Tekniske kurser

Tekniske certificeringer

Vi leverer omfattende læseplan for tekniske kompetencefærdigheder på certificeringspræstationen.

Lær hvordan

Extreme kursuskatalog

Lær hvordan

ATP-akkreditering

Som autoriseret uddannelsespartner (ATP) sikrer Insoft Services, at du får de højeste uddannelsesstandarder, der findes.

Lær hvordan

Løsninger og tjenester

Vi leverer innovativ og avanceret support til design, implementering og optimering af IT-løsninger. Vores kundebase omfatter nogle af de største Telcos globalt.

Lær hvordan

Globalt anerkendte team af certificerede eksperter hjælper dig med at gøre en mere jævn overgang med vores foruddefinerede konsulent-, installations- og migreringspakker til en lang række Fortinet-produkter.

Om os

Insoft tilbyder autoriseret uddannelses- og konsulentbistand til udvalgte IP-leverandører. Få mere at vide om, hvordan vi revolutionerer branchen.

Lær hvordan
  • +45 32 70 99 90
  • Applied Unsupervised Learning with R

    Duration
    2 Dage
    Delivery
    (Online Og På stedet)
    Price
    Pris på forespørgsel
    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/)
    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/)
      Kommende datoer
      Dato på anmodning

    Follow Up Courses

    Filtrer
    • 2 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 3 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 3 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 3 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 2 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 4 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 2 Dage
      Dato på anmodning
      Price on Request
      Book Now
    • 3 Dage
      Dato på anmodning
      Price on Request
      Book Now

    Know someone who´d be interested in this course?
    Let them know...

    Use the hashtag #InsoftLearning to talk about this course and find students like you on social media.