AI Workflow: Feature Engineering and Bias Detection

via Coursera

Overview

Delve into the crucial phases of developing AI solutions with the third installment of the IBM AI Enterprise Workflow Certification specialization, focusing on "AI Workflow: Feature Engineering and Bias Detection". This course is designed to seamlessly follow the progression from the previous two installments, hence it is highly recommended to engage with this curriculum sequentially for a comprehensive learning experience. In this module, participants will encounter advanced techniques tailored for a hypothetical media company scenario, emphasizing feature engineering, the mitigation of class imbalances, and the paramount importance of detecting and addressing bias within data to uphold the integrity of machine learning models.

Throughout this course, learners will be equipped with the knowledge to implement best practices for dimension reduction, outlier detection, and the application of unsupervised learning methods to unveil underlying patterns in data. With an array of case studies, including topic modeling and intricate data visualization, the course aims to fortify skills in several key areas:

Addressing and counteracting issues related to class imbalances
Understanding the ethical implications of data bias
Utilizing AI Fairness 360 open-source libraries for bias detection in AI models
Applying dimension reduction strategies for both exploratory data analysis (EDA) and transformation stages
Analyzing text data through topic modeling techniques and visualization
Adopting best practices in handling outliers in high-dimensional data
Integrating outlier detection algorithms for both quality assurance and modeling purposes
Incorporating unsupervised learning techniques and basic clustering algorithms into the AI workflow

This course is specifically tailored for current data science professionals with experience in building machine learning models, looking to enhance their expertise in AI deployment within large-scale enterprises. Prerequisites for enrolling include completion of the first two courses in the IBM AI Enterprise Workflow specialization, a fundamental grasp of linear algebra, sampling, probability theory and distributions, descriptive and inferential statistics, machine learning principles, and proficiency in Python alongside familiarity with data science libraries (NumPy, Pandas, matplotlib, scikit-learn), IBM Watson Studio, and the design thinking process. Offered through Coursera, this course falls under the categories of Artificial Intelligence Courses and Unsupervised Learning Courses.