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AWS DISCOVERY DAY: MACHINE LEARNING BASICS

  • Course Code GKAWS-MLB
  • Duration 1 day

Course Delivery

Public Classroom Price

Free of Charge

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Course Delivery

This course is available in the following formats:

  • Public Classroom

    Traditional Classroom Learning

  • Virtual Learning

    Learning that is virtual

Request this course in a different delivery format.

Course Overview

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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

Course Schedule

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    • Delivery Format: Virtual Learning
    • Date: 28 June, 2024

      Guaranteed  To Run

    • Location: Virtual

    Free of Charge

    • Delivery Format: Virtual Learning
    • Date: 27 September, 2024
    • Location: Virtual

    Free of Charge

    • Delivery Format: Virtual Learning
    • Date: 20 December, 2024
    • Location: Virtual

    Free of Charge

Target Audience

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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!

Course Objectives

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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?

Course Content

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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

Follow on Courses

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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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