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Cisco Sertifisering

Opplev en blandet læringstilnærming som kombinerer det beste av instruktørledet opplæring og e-læring i eget tempo for å hjelpe deg med å forberede deg til sertifiseringseksamen.

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Cisco Learning Credits

Cisco Learning Credits (CLC) er forhåndsbetalte opplæringskuponger innløst direkte med Cisco som gjør planleggingen for suksessen din enklere når du kjøper Cisco-produkter og -tjenester.

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Etterutdanning

Cisco Continuing Education Program tilbyr alle aktive sertifiseringsinnehavere fleksible alternativer for å resertifisere ved å fullføre en rekke kvalifiserte opplæringselementer.

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Cisco Digital Learning

Sertifiserte ansatte er verdsatte eiendeler. Utforsk Ciscos offisielle digitale læringsbibliotek for å utdanne deg gjennom innspilte økter.

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Cisco Business Enablement

Cisco Business Enablement Partner Program fokuserer på å skjerpe forretningsferdighetene til Cisco Channel Partners og kunder.

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Fortinet Sertifisering

Fortinet Network Security Expert (NSE)-programmet er et opplærings- og sertifiseringsprogram på åtte nivåer for å lære ingeniører om nettverkssikkerheten for Fortinet FW-ferdigheter og -erfaring.

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Insoft er anerkjent som Fortinet Autorisert Opplæringssenter på utvalgte steder i EMEA.

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Utforsk et bredt utvalg av Fortinet Schedule på tvers av forskjellige land så vel som online kurs.

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Microsoft-opplæring

Insoft Services gir Microsoft opplæring i EMEAR. Vi tilbyr Microsofts tekniske opplærings- og sertifiseringskurs som ledes av instruktører i verdensklasse.

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Extreme-opplæring

Lær eksepsjonell kunnskap og ferdigheter i ekstreme nettverk.

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Teknisk sertifisering

Vi tilbyr omfattende læreplan over tekniske kompetanseferdigheter om sertifiseringsprestasjonen.

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Extreme opplæringskatalog

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ATP-akkreditering

Som autorisert opplæringspartner (ATP) sørger Insoft Services for at du får de høyeste utdanningsstandardene som er tilgjengelige.

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Løsninger og tjenester

Vi tilbyr innovativ og avansert støtte for design, implementering og optimalisering av IT-løsninger. Vår kundebase inkluderer noen av de største Telcos globalt.

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Globalt anerkjent team av sertifiserte eksperter hjelper deg med å gjøre en jevnere overgang med våre forhåndsdefinerte konsulent-, installasjons- og migrasjonspakker for et bredt spekter av Fortinet-produkter.

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  • +47 23 96 21 03
  • CAIP - Certified Artificial Intelligence (AI) Practitioner: Exam AIP-110

    Duration
    5 Dager
    Delivery
    (Online Og På stedet)
    Price
    Pris på forespørsel

    Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

    • Specify a general approach to solve a given business problem that uses applied AI and ML.
    • Collect and refine a dataset to prepare it for training and testing.
    • Train and tune a machine learning model.
    • Finalize a machine learning model and present the results to the appropriate audience.
    • Build linear regression models.
    • Build classification models.
    • Build clustering models.
    • Build decision trees and random forests.
    • Build support-vector machines (SVMs).
    • Build artificial neural networks (ANNs).
    • Promote data privacy and ethical practices within AI and ML projects.

    Lesson 1: Solving Business Problems Using AI and ML

    • Topic A: Identify AI and ML Solutions for Business Problems
    • Topic C: Formulate a Machine Learning Problem
    • Topic D: Select Appropriate Tools

     

    Lesson 2: Collecting and Refining the Dataset

    • Topic A: Collect the Dataset
    • Topic B: Analyze the Dataset to Gain Insights
    • Topic C: Use Visualizations to Analyze Data
    • Topic D: Prepare Data

     

    Lesson 3: Setting Up and Training a Model

    • Topic A: Set Up a Machine Learning Model
    • Topic B: Train the Model

     

    Lesson 4: Finalizing a Model

    • Topic A: Translate Results into Business Actions
    • Topic B: Incorporate a Model into a Long-Term Business Solution

     

    Lesson 5: Building Linear Regression Models

    • Topic A: Build a Regression Model Using Linear Algebra
    • Topic B: Build a Regularized Regression Model Using Linear Algebra
    • Topic C: Build an Iterative Linear Regression Model

     

    Lesson 6: Building Classification Models

    • Topic A: Train Binary Classification Models
    • Topic B: Train Multi-Class Classification Models
    • Topic C: Evaluate Classification Models
    • Topic D: Tune Classification Models

     

    Lesson 7: Building Clustering Models

    • Topic A: Build k-Means Clustering Models
    • Topic B: Build Hierarchical Clustering Models

     

    Lesson 8: Building Advanced Models

    • Topic A: Build Decision Tree Models
    • Topic B: Build Random Forest Models

     

    Lesson 9: Building Support-Vector Machines

    • Topic A: Build SVM Models for Classification
    • Topic B: Build SVM Models for Regression

     

    Lesson 10: Building Artificial Neural Networks

    • Topic A: Build Multi-Layer Perceptrons (MLP)
    • Topic B: Build Convolutional Neural Networks (CNN)

     

    Lesson 11: Promoting Data Privacy and Ethical Practices

    • Topic A: Protect Data Privacy
    • Topic B: Promote Ethical Practices
    • Topic C: Establish Data Privacy and Ethics Policies

     

    Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)

    The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.

     

    So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.

     

    A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming.

     

    This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

    To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.

     

    You can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course. You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following Logical Operations or comparable course:

    • Database Design: A Modern Approach
    • Python® Programming: Introduction
    • Python® Programming: Advanced

    Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

    • Specify a general approach to solve a given business problem that uses applied AI and ML.
    • Collect and refine a dataset to prepare it for training and testing.
    • Train and tune a machine learning model.
    • Finalize a machine learning model and present the results to the appropriate audience.
    • Build linear regression models.
    • Build classification models.
    • Build clustering models.
    • Build decision trees and random forests.
    • Build support-vector machines (SVMs).
    • Build artificial neural networks (ANNs).
    • Promote data privacy and ethical practices within AI and ML projects.

    Lesson 1: Solving Business Problems Using AI and ML

    • Topic A: Identify AI and ML Solutions for Business Problems
    • Topic C: Formulate a Machine Learning Problem
    • Topic D: Select Appropriate Tools

     

    Lesson 2: Collecting and Refining the Dataset

    • Topic A: Collect the Dataset
    • Topic B: Analyze the Dataset to Gain Insights
    • Topic C: Use Visualizations to Analyze Data
    • Topic D: Prepare Data

     

    Lesson 3: Setting Up and Training a Model

    • Topic A: Set Up a Machine Learning Model
    • Topic B: Train the Model

     

    Lesson 4: Finalizing a Model

    • Topic A: Translate Results into Business Actions
    • Topic B: Incorporate a Model into a Long-Term Business Solution

     

    Lesson 5: Building Linear Regression Models

    • Topic A: Build a Regression Model Using Linear Algebra
    • Topic B: Build a Regularized Regression Model Using Linear Algebra
    • Topic C: Build an Iterative Linear Regression Model

     

    Lesson 6: Building Classification Models

    • Topic A: Train Binary Classification Models
    • Topic B: Train Multi-Class Classification Models
    • Topic C: Evaluate Classification Models
    • Topic D: Tune Classification Models

     

    Lesson 7: Building Clustering Models

    • Topic A: Build k-Means Clustering Models
    • Topic B: Build Hierarchical Clustering Models

     

    Lesson 8: Building Advanced Models

    • Topic A: Build Decision Tree Models
    • Topic B: Build Random Forest Models

     

    Lesson 9: Building Support-Vector Machines

    • Topic A: Build SVM Models for Classification
    • Topic B: Build SVM Models for Regression

     

    Lesson 10: Building Artificial Neural Networks

    • Topic A: Build Multi-Layer Perceptrons (MLP)
    • Topic B: Build Convolutional Neural Networks (CNN)

     

    Lesson 11: Promoting Data Privacy and Ethical Practices

    • Topic A: Protect Data Privacy
    • Topic B: Promote Ethical Practices
    • Topic C: Establish Data Privacy and Ethics Policies

     

    Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)

    The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.

     

    So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.

     

    A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming.

     

    This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

    To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.

     

    You can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course. You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following Logical Operations or comparable course:

    • Database Design: A Modern Approach
    • Python® Programming: Introduction
    • Python® Programming: Advanced
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