Practical Decision-Making Using No-code ML on AWS

• When will I have access to the lectures and assignments?

  Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

  • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

  • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

• What will I get if I purchase the Certificate?

  When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

• What is the refund policy?

  You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.

• Is financial aid available?

  Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

• What types of machine learning does this cover?

  In this course, we cover both supervised learning and unsupervised learning, however this course is based on supervised learning. The course predominantly focuses on using structured tabular data, such as spreadsheets and database tables, to explain and predict a specific variable (structured supervised machine learning). The course also covers other types like unstructured and unsupervised methods of machine learning.

• What tools does this course cover?

  This course covers Amazon SageMaker Canvas, which provides a visual point-and-click interface to build ML models and generate accurate predictions—no code required. With Amazon SageMaker Canvas, you can quickly connect, access, and combine data, and create unified datasets for training ML models with automatic data cleansing, automatically create ML models so you can generate accurate predictions—whether singular or in bulk, and share models and datasets with data scientists so they can validate and further refine ML models.

• Do I need machine learning experience or coding experience to take this course?

  No! You do not need any prior machine learning or coding experience to take this course, but you do need critical thinking skills.

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