The main focus of this course is to understand the general principles behind assessing the classification performance of an AI system, the different aspects that can affect classification performance, and a set of basic metrics that we can use to evaluate a classifier.
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 gain:
- Awareness of the main factors affecting performance: you will gain a clear idea of what control criteria is available in the assessment of machine learning classification
- Understanding of the general process of assessing performance in a classification-based AI system
- Awareness of performance assessment techniques: understanding of the main measures available to quantify performance in the context of classification
- Common grounds to compare 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 and team leaders
What will I learn?
Upon completion of this course, learners will be able to:
- Understand the purpose of assessing classification performance
- Understand the general process to assess performance in classification-based AI systems
- Apply well known measures, metrics to quantify performance when designing or evaluating AI models in general
- Establish a common ground to compare multiple classifier models
What is included?
On completion, you’ll be awarded an internationally recognized BSI training course certificate.
