Frame Set

Problem Statement

Machine learning models depend heavily on the information used during development. However, raw datasets may contain missing values, inconsistent formats, duplicated records, unusual observations, or features that require additional preparation.

Without a structured approach to data preparation, it can be difficult to understand what information a model receives and how dataset decisions relate to later evaluation.

Solution

Frame Set provides a structured approach to examining and preparing data for machine learning workflows. Learners explore the steps between receiving raw information and creating organized datasets suitable for model development and evaluation.

The course also explains why documenting preparation decisions is an important part of maintaining understandable and repeatable engineering workflows.

What’s Inside

Frame Set contains detailed modules covering dataset structure, data inspection, cleaning principles, missing values, feature preparation, dataset splitting, preprocessing workflows, and documentation.

Practical examples and guided activities illustrate how different preparation decisions can influence the information available during model development.

Who Is This For?

Frame Set is intended for learners who already understand the basic stages of Machine Learning Engineering and want to study data preparation in greater detail.

It can also support learners who want to develop a more organized approach to examining datasets before moving into model development and evaluation.

What You’ll Learn

• Examine the structure and characteristics of a dataset.
• Identify missing, duplicated, and inconsistent information.
• Explore approaches to handling incomplete observations.
• Understand common data cleaning principles.
• Organize numerical and categorical information.
• Explore feature preparation and representation.
• Understand the purpose of training, validation, and test datasets.
• Recognize situations where data leakage may occur.
• Examine how preprocessing decisions relate to model behavior.
• Build structured data preparation workflows.
• Document data changes and preparation decisions.
• Review prepared datasets before model development begins.

Guarantee

Frame Set includes a 30-day refund period. Learners can review the course materials during this period and request a refund according to the applicable refund policy.

The refund policy provides defined terms for requesting a refund during the stated period.