AWS DISCOVERY DAY: MACHINE LEARNING BASICS
- Course Code GKAWS-MLB
- Duration 1 day
Course Delivery
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Course Delivery
This course is available in the following formats:
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Public Classroom
Traditional Classroom Learning
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopLearn 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
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Date:
20 December, 2024
Guaranteed To Run
- Location: Virtual
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- Delivery Format: Virtual Learning
- Date: 06 March, 2025
- Location: Virtual
- Language: French
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- Delivery Format: Virtual Learning
- Date: 06 June, 2025
- Location: Virtual
- Language: French
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- Delivery Format: Virtual Learning
- Date: 08 September, 2025
- Location: Virtual
- Language: French
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- Delivery Format: Virtual Learning
- Date: 08 December, 2025
- Location: Virtual
- Language: French
Target Audience
TopThis event is intended for:
- Developers
- Solution architects
- Data engineers
- Individuals interested in building solutions with machine learning - no machine learning experience required!
Course Objectives
TopDuring 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
TopSection 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
TopCourses
- 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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