Discovery Day- Machine Learning Basics (AWSDD-MLB)

 

Course Overview

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

We recommend that attendees of this event continue learning with these:

Who should attend

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

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?

Follow On Courses

Outline: Discovery Day- Machine Learning Basics (AWSDD-MLB)

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

Prices & Delivery methods

Online Training

Duration
2 hours

Price
  • Online Training: Free offering
  • Online Training: Free offering

Schedule

Currently there are no training dates scheduled for this course.