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AWS DISCOVERY DAY: MACHINE LEARNING BASICS

  • Code training GKAWS-MLB
  • Duur 1 dag

Andere trainingsmethoden

Klassikale training Prijs

Kosteloos

Vraag een groepstraining aan Schrijf je in

Methode

Deze training is in de volgende formats beschikbaar:

  • Klassikale training

    Klassikaal leren

  • Virtueel leren

    Virtueel leren

Vraag deze training aan in een andere lesvorm.

Trainingsbeschrijving

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Learn about important concepts, terminology, and the phases of a machine learning pipeline.

Are you interested in machine learning, but not sure where to start? Join us for this session with an AWS expert and demystify the basics. Using real-world examples, you’ll learn about important concepts, terminology, and the phases of a machine learning pipeline. Learn how you can unlock new insights and value for your business using machine learning.

- Level: Fundamental
- Duration: 1.5 hours

    • Methode: Virtueel leren
    • Datum: 28 juni, 2024

      Startgarantie

    • Locatie: Virtueel-en-klassikaal
    • Taal: Engels

    Kosteloos

    • Methode: Virtueel leren
    • Datum: 27 september, 2024
    • Locatie: Virtueel-en-klassikaal
    • Taal: Engels

    Kosteloos

    • Methode: Virtueel leren
    • Datum: 20 december, 2024
    • Locatie: Virtueel-en-klassikaal
    • Taal: Engels

    Kosteloos

Doelgroep

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This event is intended for:

- Developers
- Solution architects
- Data engineers
- Individuals interested in building solutions with machine learning - no machine learning experience required!

Trainingsdoelstellingen

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During this event, you will learn:

  • What is Machine Learning?
  • What is the machine learning pipeline, and what are its phases?
  • What is the difference between supervised and unsupervised learning?
  • What is reinforcement learning?
  • What is deep learning?

Inhoud training

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Section 1: Machine learning basics

  • Classical programming vs. machine learning approach
  • What is a model?
  • Algorithm features, weights, and outputs
  • Machine learning algorithm categories
  • Supervised algorithms
  • Unsupervised algorithms
  • Reinforcement learning

Section 2: What is deep learning?

  • How does deep learning work?
  • How deep learning is different

Section 3: The Machine Learning Pipeline

  • Overview
  • Business problem
  • Data collection and integration
  • Data processing and visualization
  • Feature engineering
  • Model training and tuning
  • Model evaluation
  • Model deployment

Section 4: What are my next steps?

  • Resources to continue learning

Vervolgtrainingen

Naar boven

Courses

  • Deep Learning on AWS
  • MLOps Engineering on AWS
  • Practical Data Science with Amazon SageMaker
  • The Machine Learning Pipeline on AWS

Resources

  • AWS Ramp-Up Guide: Machine Learning
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