The focus of this course is to understand the general principles behind assessing the classification performance of an AI system. We will explore, in more detail, the different available metrics for three different classification scenarios (binary, multi-class, and multi-label), the available statistical significance tests that are appropriate for classification, and some important computational complexity metrics.
On-demand - training that’s even more flexible
BSI’s on-demand courses are market-leading and available 24/7. Developed by top subject matter experts, they contain the same high-quality content you will find in our tutor-led training, but with the added benefit of being able to learn at your own pace and at any time.
How will I benefit?
This course will help learners to:
- Understand the general process of assessing performance in a classification-based AI system
- Obtain deeper knowledge of performance assessment techniques: understanding of the main measures available to quantify performance in the context of classification
- To establish a common ground to compare and evaluate models: by incorporating these concepts early in the design lifecycle of an AI application, you’ll be able to establish clear grounds to establish how good is the model you are working on, and to compare it to other models in a sensible way
Who should attend?
AI managers, team leaders, and machine learning practitioners in general
What will I learn?
Upon completion of this course, learners will be able to:
- Understand the purpose of assessing classification performance
- Use the all the metrics and measures mentioned in the standard to evaluate classifiers in three different scenarios: binary, multi-class, and multi-label
- Use statistical significance testing to establish comparisons with different models or versions of the same model
- Use metrics to evaluate the computational complexity of your model
What is included?
On completion, you’ll be awarded an internationally recognized BSI training course certificate.
