Planning a Machine Learning Project
This course introduces requirements to determine if machine learning (ML) is the appropriate solution to a business problem. • Course level: Fundamental • Duration: 30 minutes Activities: This course includes presentations, videos, and knowledge assessments. Course objectives: In this course, you will learn to: • Identify the data, time, and production requirements for a successful ML project Intended audience: This course is intended for: • Nontechnical business leaders and other business decision makers who are, or will be, involved in ML projects • Participants of the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshops Prerequisites: We recommend that attendees of this course have: • Introduction to Machine Learning: Art of the Possible Course outline: Module 1: Is a machine learning solution appropriate for my problem? • Explain how to determine if ML is the appropriate solution to your business problem Module 2: Is my data ready for machine learning? • Describe the process of ensuring that your data is ML ready Module 3: How will machine learning impact a project timeline? • Explain how ML can impact a project timeline Module 4: What early questions should I ask in deployment? • Identify the questions to ask that affect ML deploymentModule 5: Conclusion
Math for Machine Learning
To understand modern machine learning, you also need to understand vectors and matrices, linear algebra, probability theorems, univariate calculus, and multivariate calculus. This course, led by AWS Machine Learning Instructor Brent Werness, covers it all.
Introduction to Machine Learning: Art of the Possible
This digital course is designed to help business decision makers understand the fundamentals of machine learning (ML). • Course level: Fundamental • Duration: 30 minutes Activities: This course includes presentations, videos, and knowledge assessments. Course objectives: In this course, you will learn to: •Understand the basics of machine learning to help evaluate the benefits and risks associated with adopting ML in various business cases Intended audience: This course is intended for: •Nontechnical business leaders and other business decision makers who are, or will be, involved in ML projects •Participants of the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshops Prerequisites: We recommend that attendees of this course have: •Basic knowledge of computers and computer systems •Some basic knowledge of the concept of machine learning Course outline: Module 1: How can machine learning help? •Define artificial intelligence •Define machine learning •Describe the different business domains impacted by machine learning •Describe the positive feedback loop (flywheel) that drives ML projects • Describe the potential for machine learning in underutilized markets Module 2: How does machine learning work? •Describe artificial intelligence •Describe the difference between artificial intelligence and machine learning Module 3: What are some potential problems with machine learning? •Describe the differences between simple and complex models •Understand unexplainability and uncertainty problems with machine learning models Module 4: Conclusion
Building a Machine Learning Ready Organization
Course description
This course provides components needed for successful organizational adoption of machine learning (ML). • Course level: Fundamental • Duration: 30 minutes Activities: This course includes presentations, videos, and knowledge assessments. Course objectives: In this course, you will learn to: • Describe how to adapt an organization to achieve and sustain success using ML Intended audience: This course is intended for: • Nontechnical business leaders and other business decision makers who are, or will be, involved in ML projects • Participants of the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshops Prerequisites: We recommend that attendees of this course have: • Introduction to Machine Learning: Art of the Possible • Planning a Machine Learning Project Course outline: Module 1: How can I prepare my organization for using ML?: • How can I prepare my organization for using ML? • How can AWS help me? • What other strategies can I adopt to ensure organizational success? • Which cultural shift-approach works for my organization? Module 2: How do I evaluate my data strategy?: • How do I evaluate my data strategy? • How can I improve my data strategy? Module 3: How do I create a culture of learning and collaboration?: • How do I create a culture of learning and collaboration? • What is a data scientist? • What skills should a data scientist have? • What does a pilot ML team look like? • What other supporting roles will I need? • What are the key responsibilities? Module 4: How do I start my ML journey?: • How do I start my ML journey? • What does an organization’s ML journey look like? • What is an example business case for an organization’s progression? Module 5: Conclusion:
AWS DeepRacer: Driven by Reinforcement Learning
This course is designed to give you hands-on experience with reinforcement learning (RL) by helping you build, train and deploy models in AWS DeepRacer so you can compete in the official AWS DeepRacer League. The course starts off by orienting you to AWS DeepRacer before diving into RL. You’ll have several opportunities to build, train, and evaluate your own RL models, and then deploy them to the physical car. By the end of the course, you’ll have your own optimized RL model that you can enter into a virtual or physical circuit of the AWS DeepRacer League.
Migrating to AWS: A high level introduction
This course provides an overview of the key topics and target audience of the Migrating to AWS classroom course. Additionally, this course will show demonstrations from the Migrating to AWS classroom course including using AWS Migration Hub to track migration projects and employing AWS Database Migration Service to migrate databases to Amazon Aurora • Course level: Fundamental • Duration: 40 minutes Activities: This course includes demonstrations and videos. Course objectives: In this course, you will learn to: • State the Target Audience, Key Concepts, and Prerequisites of the Migrating to AWS classroom course • Identify the main tasks of a successful migration project • List key AWS Migration Tools and Services • Describe how to use AWS Migration Hub to track migration projects • Describe how to use AWS Database Migration Service to migrate databases Intended audience: This course is intended for: • Individuals who are involved in the planning and execution of migration projects Prerequisites: We recommend that attendees of this course have: • There are no prerequisites for this course  Course outline: • Target Audience, Key Concepts, and Prerequisites of the Migrating to AWS Classroom Course • Key AWS Migration Services and Tools • Demonstration: Track Migration Projects with Migration Hub • Demonstration: Migrate Databases with Database Migration Service
AWS Foundations: Machine Learning Basics
What is machine learning? How can machine learning solve business problems? When is it appropriate to use a machine learning model? What are the phases of a machine learning pipeline? In this course, you get an overview of the concepts, terminology, and processes of the exciting field of machine learning! •Course level: Fundamental •Delivery method: Digital training •Duration: 30 minutes Course objectives: In this course, you will learn to: •Explain machine learning •Describe the three categories of machine learning algorithms •Explain deep learning •Describe the machine learning pipeline phases Intended audience: This course is intended for: •Developers •Solution Architects •Data Engineers •Anyone who wants to learn about the machine learning pipeline Prerequisites: We recommend that attendees of this course have: •Basic understanding of the AWS Cloud infrastructure Course outline: •Machine learning •Deep learning •The machine learning pipeline
