Course Description
These lessons will explain the typical model-building process in reference to the classification model pipeline. You will also receive an introduction to binary and multi-class classification problems, and the ways in which they are best used. You will learn how to use feature engineering to figure out the best data inputs for your classification models.
Additionally, these lessons will discuss and outline modeling with quality metrics. You will learn how to assess the quality of a classification model, and how to validate a classification model before its deployment.
What You'll Learn
- Follow the typical process for building a classification model, from the model-building pipeline through deployment
- Apply feature engineering to identify the best data inputs for classification models
- Distinguish between binary and multi-class classification problems and when each is best used
- Evaluate classification model performance using model quality metrics
- Validate a classification model before deploying it
- Build classification models for both binary and multiclass classification problems
Key Takeaways
- The course explains the typical model-building process in reference to the classification model pipeline.
- Feature engineering is used to figure out the best data inputs for classification models.
- Binary and multi-class classification problems differ, and each has scenarios in which it is best used.
- Quality metrics allow you to assess the quality of a classification model.
- A classification model should be validated before its deployment.
Frequently Asked Questions
What does this course cover?
It covers the typical model-building process for the classification model pipeline, an introduction to binary and multi-class classification problems, feature engineering for choosing the best data inputs, modeling with quality metrics, and model validation methods.
What is the difference between binary and multi-class classification covered here?
The course introduces both binary and multi-class classification problems and discusses the ways in which each is best used.
How does the course address model quality and deployment readiness?
It discusses and outlines modeling with quality metrics, teaching how to assess the quality of a classification model and how to validate a classification model before its deployment.
What skills will I gain from this course?
You will build skills in Data Classification, Feature Engineering, Feature Learning, Feature Selection, Machine Learning Model Training, and Statistical Classification.
What topics are taught in the lessons?
The lessons cover the model building process, feature engineering, binary classification problems, model quality metrics, multiclass classification problems, and model validation methods.
