insoft

Formazione Cisco

Insoft Services è uno dei pochi fornitori di formazione in EMEAR a offrire una gamma completa di certificazione Cisco e formazione tecnologica specializzata.

Dettagli

Certificazioni Cisco

Sperimenta un approccio di apprendimento misto che combina il meglio della formazione con istruttore e dell'e-learning autogestito per aiutarti a prepararti per l'esame di certificazione.

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Cisco Learning Credits

I Cisco Learning Credits (CLC) sono voucher di formazione prepagati riscattati direttamente con Cisco che semplificano la pianificazione del successo durante l'acquisto di prodotti e servizi Cisco.

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Formazione Continua

The Cisco Continuing Education Program offers all active certification holders flexible options to recertify by completing a variety of eligible training items.

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Cisco Digital Learning

Certified employees are VALUED assets. Explore Cisco official Digital Learning Library to educate yourself through recorded sessions.

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Cisco Business Enablement

The Cisco Business Enablement Partner Program focuses on sharpening the business skills of Cisco Channel Partners and customers.

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Catalogo Cisco

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Certificazioni Fortinet

Il programma Fortinet Network Security Expert (NSE) è un programma di formazione e certificazione di otto livelli per insegnare agli ingegneri la sicurezza della loro rete per le competenze e l'esperienza di Fortinet FW.

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Corsi di formazione tecnica

Insoft è riconosciuto come Fortinet Authorized Training Center in sedi selezionate in tutta l'EMEA.

Corsi tecnici

Catalogo Fortinet

Esplora un'ampia varietà di programmi Fortinet in diversi paesi e corsi online.

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Stato ATC

Controlla il nostro stato ATC in tutti i paesi selezionati in Europa.

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Fortinet Servizi Professionale

Il team riconosciuto a livello globale di esperti certificati ti aiuta a fare una transizione più fluida con i nostri pacchetti di consulenza, installazione e migrazione predefiniti per una vasta gamma di prodotti Fortinet.

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Catalogo Microsoft

Insoft Services fornisce formazione Microsoft in EMEAR. Offriamo corsi di formazione tecnica e certificazione Microsoft guidati da istruttori di livello mondiale.

Corsi tecnici

Corsi di formazione

Impara conoscenze e abilità eccezionali di Extreme Networks.Find all the Extreme Networks online and instructor led class room based calendar here.

Corsi tecnici

Certificazioni Extreme

Forniamo un curriculum completo di competenze tecniche sul conseguimento della certificazione.

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Catalogo Extreme

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Accreditamento ATP

In qualità di partner di formazione autorizzato (ATP), Insoft Services garantisce che tu riceva i più alti standard di istruzione disponibili.

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Pacchetti di consulenza

Forniamo un supporto innovativo e avanzato per la progettazione, l'implementazione e l'ottimizzazione delle soluzioni IT.La nostra base di clienti comprende alcune delle più grandi telco a livello globale.

Soluzioni & Servizi

Il team riconosciuto a livello globale di esperti certificati ti aiuta a fare una transizione più fluida con i nostri pacchetti di consulenza, installazione e migrazione predefiniti per una vasta gamma di prodotti Fortinet.

Chi siamo

Insoft fornisce servizi di formazione e consulenza autorizzati per fornitori IP selezionati.Scopri come stiamo rivoluzionando il settore.

Dettagli
  • +39 02 8704 5199
  • Prenota ora

    • Applied Unsupervised Learning with R

    Enroll Now
    Duration
    2 Giorni
    Delivery
    (Online e in loco)
    Price
    Prezzo su richiesta
    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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      Data su richiesta

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