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Quickly Build and Train Machine Learning Models with IBM AutoAI

  • Référence W7L155G
  • Durée 1 Jour

Classe inter à distance Prix

EUR790,00

hors TVA

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Modalité pédagogique

La formation est disponible dans les formats suivants:

  • Classe inter à distance

    Depuis n'importe quelle salle équipée d'une connexion internet, rejoignez la classe de formation délivrée en inter-entreprises.

  • Classe inter en présentiel

    Formation délivrée en inter-entreprises. Cette méthode d'apprentissage permet l'interactivité entre le formateur et les participants en classe.

  • Intra-entreprise

    Cette formation est délivrable en groupe privé, et adaptable selon les besoins de l’entreprise. Nous consulter.

Demander cette formation dans un format différent

Quickly build and train machine learning models with Watson AutoAI aims to familiarize data science and analytics professionals with the fundamentals of theWatson Studios AutoAI tool. The course walks users through the process of creatingIBM Cloud projects, and building and evaluating AutoAI experiments for various supervised machine learning use cases. 

The lab environment for this course uses IBM Cloud.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Prochaines dates

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    • Modalité: Classe inter à distance
    • Date: 30 août, 2024
    • Centre: SITE DISTANT

    EUR790,00

    • Modalité: Classe inter à distance
    • Date: 15 novembre, 2024
    • Centre: SITE DISTANT

    EUR790,00

    • Modalité: Classe inter à distance
    • Date: 11 février, 2025
    • Centre: SITE DISTANT

    EUR790,00

This course is designed for data analysts, data scientists, and machine learning specialists.

Objectifs de la formation

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  • Set up an IBM Cloud Account and project and add and manage associated resources
  • Identify potential machine learning use cases applicable to AutoAI
  • Differentiate problem types relevant for AutoAI experiments (Classification, Regression, Time Series)
  • Configure settings for various AutoAI experiments
  • Evaluate pipelines and models that are produced by AutoAI experiments
  • Recognize deployment strategies for AutoAI models

Programme détaillé

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  • Course introduction
  • Introducing IBM Cloud
  • What is AutoAI?
  • Machine learning with AutoAI: Classification
  • Machine learning with AutoAI: Regression
  • AutoAI deployments at a glance
  • Machine learning with AutoAI: Time series
  • Fairness evaluation in AutoAI machine learning models

Pré-requis

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  • IBM Cloud access
  • Knowledge of supervised machine learning use cases
  • Knowledge of Python code in notebook environments
  • Basic knowledge of machine learning evaluation metrics
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