AI Workflow: Feature Engineering and Bias Detection (Coursera)

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:

1. Employ the tools that help address class and class imbalance issues
2. Explain the ethical considerations regarding bias in data
3. Employ ai Fairness 360 open source libraries to detect bias in models
4. Employ dimension reduction techniques for both EDA and transformations stages
5. Describe topic modeling techniques in natural language processing
6. Use topic modeling and visualization to explore text data
7. Employ outlier handling best practices in high dimension data
8. Employ outlier detection algorithms as a quality assurance tool and a modeling tool
9. Employ unsupervised learning techniques using pipelines as part of the AI workflow
10. Employ basic clustering algorithms

Course 3 of 6 in the IBM AI Enterprise Workflow Specialization.

Syllabus

WEEK 1
Data transforms and feature engineering
This module will introduce you to skills required for effective feature engineering in today's business enterprises. The skills are presented as a series of best practices representing years of practical experience.

WEEK 2
Pattern recognition and data mining best practices
This module will continue the discussion of skill related to feature engineering for practicing data scientists, with a focus on outliers and the use of unsupervised learning techniques for finding patterns.