Course Description
In these lessons, you will learn how to choose the right model by considering the trade-offs between them. You will also learn how to deal with common challenges faced in classification models, including bias, fairness, and data collection problems.
These lessons will explore the balance between accuracy and explainability, as well as remaining aware of groups your models can affect. You will learn about some of the current problems with classification systems in terms of adversarial attacks that can destroy model performance and cause major problems in deployment. Finally, you will learn about emerging challenges and potential legislation in classification modeling.
What You'll Learn
- Choose the right classification model by weighing the trade-offs between different models
- Address common challenges in classification models, including bias, fairness, and data collection problems
- Balance accuracy against explainability when building classification models
- Identify the groups your models can affect and remain aware of their impact
- Recognize adversarial attacks that can destroy model performance and cause problems in deployment
- Explore emerging challenges and potential legislation in classification modeling
Key Takeaways
- Choosing a classification model involves weighing the trade-offs between the available models.
- Classification models face common challenges such as bias, fairness, and data collection problems.
- There is a balance to consider between a model's accuracy and its explainability.
- Adversarial attacks can destroy model performance and cause major problems in deployment.
- Classification modeling faces emerging challenges and potential legislation.
Frequently Asked Questions
What does this course cover?
The course covers how to choose the right classification model by considering trade-offs, common challenges such as bias, fairness, and data collection problems, the balance between accuracy and explainability, adversarial attacks, and emerging challenges and potential legislation in classification modeling.
What topics are included in the lessons?
The lessons include Choosing the Right Model, Data collection problems, Explainability, Bias and Fairness, Adversarial Attacks, and Emerging Challenges.
What skills will I gain from this course?
The course develops skills in Data Classification, Feature Learning, Learning Metrics, Machine Learning, Machine Learning Model Training, and Statistical Classification.
Does the course address fairness and bias in models?
Yes. The lessons cover bias and fairness as common challenges in classification models and emphasize remaining aware of the groups your models can affect.
Does the course discuss security risks to classification models?
Yes. It covers adversarial attacks that can destroy model performance and cause major problems in deployment.
