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AWS DISCOVERY DAY: MACHINE LEARNING BASICS

  • Course Code GKAWS-MLB
  • Duration 1 day

Course Delivery

Virtual Learning 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

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.

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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