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:
-
Public Classroom
Traditional Classroom Learning
-
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
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
TopTarget 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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