Machine Learning Basics
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
Key topics covered
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?
Recommended follow-up training and resources
We recommend that attendees of this event continue learning with these:
Deep Learning on AWS
MLOps Engineering on AWS
Practical Data Science with Amazon SageMaker
The Machine Learning Pipeline on AWS
AWS Ramp-Up Guide: Machine Learning
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
This event is intended for:
· Solution architects
· Data engineers
· Individuals interested in building solutions with machine learning - no machine learning experience required!
Section 3: The Machine Learning Pipeline
· 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