Feature Engineering and Bias Detection

Feature Engineering and Bias Detection are among the best online feature engineering courses. This is the third course in the IBM AI Enterprise Workflow Certification specialization. You are strongly encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

Feature Engineering and Bias Detection introduce you to the next stage of the workflow for our hypothetical media company. In this stage of work, you will learn best practices for feature engineering, handling class imbalances, and detecting bias in the data. Class imbalances can seriously affect the validity of your machine learning models, and the mitigation of bias in data is essential to reducing the risk associated with biased models. These topics will be followed by sections on best practices for dimension reduction, outlier detection, and unsupervised learning techniques for finding patterns in your data. The case studies will focus on topic modeling and data visualization.

By the end of this course you will be able to:

• Employ the tools that help address class and class imbalance issues
• Explain the ethical considerations regarding bias in data
• Employ ai Fairness 360 open source libraries to detect bias in models
• Employ dimension reduction techniques for both EDA and transformations stages
• Describe topic modeling techniques in natural language processing
• Employ outlier detection algorithms as a quality assurance tool and a modeling tool
• Employ unsupervised learning techniques using pipelines as part of the AI workflow

This course offers:

• Flexible deadlines: Reset deadlines based on your availability.
• Get a Certificate when you complete
• 100% online
• Course 3 of 6 in the IBM AI Enterprise Workflow Specialization
• Advanced level
• Approximately 12 hours to complete
• Subtitles: English

Coursera Rating: 4.4/5

Enroll here: https://www.coursera.org/learn/ibm-ai-workflow-feature-engineering-bias-detection