This course introduces students to the concepts and terminology of Artificial Intelligence (AI), machine learning, and generative AI. By the end of this course, students will be able to select and apply machine learning services to resolve business problems. They will also be able to label, build, train, and deploy a custom machine learning model through a guided, hands-on approach.
Job Outlook
According to the Bureau of Labor Statistics, employment of Data Scientists is projected to grow 35% from 2022 to 2032, much faster than the average for all occupations.
FAQs
Question: Does this course prepare students for an AWS certification?
Answer: Amazon Web Services has recently updated their certification paths with several new credentials in Machine Learning and Data Science. This introductory course will build the foundation students will need to prepare for these new exams and provide the skills needed to enter this growing field.
Course Objectives
• Describe machine learning (ML)
• Implement a machine learning pipeline using Amazon SageMaker
• Use managed Amazon ML services for forecasting
• Use managed Amazon ML services for computer vision
• Use managed Amazon ML services for natural language processing
• Identify how Amazon ML services for generative AI are used
Prerequisites
• Completed an introductory cloud computing course.
• Experience scripting with Python or equivalent
• A basic understanding of statistics
Curriculum
Subject 1 – Introduction to AWS Academy Machine Learning Foundations
Subject 2 – Introduction to Machine Learning
Subject 3 – Implementing a Machine Learning pipeline with Amazon SageMaker
Subject 4 – Introduction to Forecasting
Subject 5 – Introduction to Computer Vision (CV)
Subject 6 – Introduction to Natural Language Processing
Subject 7 – Introduction to Generative AI
Subject 8 – Course Wrap-Up
Registration and Enrollment
This course will be delivered in-person. Add to cart and check out.
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