You’ve probably heard about how machine learning is shaping our world–from facial recognition to package delivery, speech recognition to self-driving cars. But how do you get started in this exciting field? In this course, Fundamentals of Machine Learning on AWS, you’ll learn how to solve business problems with AWS machine learning technologies. First, you’ll explore what ML is and how it relates to artificial intelligence and deep learning. Next, you’ll learn how to identify and frame opportunities for machine learning. Then, you’ll discover the end-to-end machine learning process: fetching, cleaning and preparing data, training and evaluating models, and deploying and monitoring models. Finally, you’ll learn the AWS artificial intelligence and machine learning technologies that enable this process, and see them in action with Amazon SageMaker Studio. When you’re finished with this course, you’ll have the skills and knowledge of AWS machine learning technologies needed to solve real-world problems. This course will also lay the foundation for the AWS Machine Learning Specialty certification.
Author Name: Amber Israelsen
Author Description:
Amber has been a software developer and technical trainer for over two decades, sharing her expertise in AI, machine learning, AWS, and Power Apps with students around the world. She has a knack for making complex tech topics easy to grasp, whether you’re a developer, designer, or business professional. Amber holds certifications in machine learning, AWS, and various Microsoft technologies, including her experience as a former Microsoft Certified Trainer. With a strong background in visual com… more

Table of Contents

• Course Overview
  1min
• Course Introduction
  13mins
• Identifying Opportunities for Machine Learning
  18mins
• Defining Machine Learning Problems
  12mins
• Fetching and Preparing Data
  37mins
• Training and Evaluating the Model
  25mins
• Deploying and Monitoring the Model
  12mins
• The AWS Machine Learning Stack
  9mins
• Next Steps
  7mins