AI Workflow: Feature Engineering and Bias Detection
This course is part of IBM AI Enterprise Workflow Specialization.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM AI Enterprise Workflow Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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Instructors:
English
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What you'll learn
Handle class imbalances and data bias effectively
Implement dimensionality reduction techniques
Utilize IBM AI Fairness 360 for bias detection
Develop topic modeling and clustering solutions
Apply outlier detection best practices
Skills you'll gain
This course includes:
0.8 Hours PreRecorded video
10 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 2 modules in this course
This comprehensive course focuses on advanced feature engineering techniques and bias detection in AI systems. Students learn to handle class imbalances, detect and mitigate bias, perform dimensionality reduction, and implement unsupervised learning methods. The curriculum covers AI Fairness 360 toolkit, topic modeling, outlier detection, and clustering algorithms, with practical case studies in text analysis and data visualization.
Data transforms and feature engineering
Module 1 · 5 Hours to complete
Pattern recognition and data mining best practices
Module 2 · 6 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: IBM AI Enterprise Workflow Specialization
Instructors
Digital Content Delivery Lead at IBM with Extensive Experience in Information Technology Education
Mark J. Grover is a Digital Content Delivery Lead at IBM, specializing in the creation and delivery of online educational content. Before joining IBM, he was a full-time professor of computer technology at Cape Fear Community College in Wilmington, NC, where he coordinated the Information Security program and taught various courses including Computer Security and Network Administration. Grover has over 25 years of experience in information technology and has received accolades such as the Cisco Instructor of Excellence award and the Award for Excellence in Innovation from the University of North Carolina Wilmington. He is passionate about outdoor activities like camping and mountain biking, and enjoys spending time with his family.
Data Science Curriculum Leader at IBM
Dr. Ray Lopez is a seasoned technical and educational expert with over 30 years of experience in software development, system administration, and research in neuroscience and artificial intelligence. Currently serving as the Data Science Curriculum Leader at IBM, he focuses on developing education and certification programs in data science. Dr. Lopez has a rich background as a university lecturer, teaching subjects such as science, mathematics, statistics, and philosophy. His extensive work includes leading initiatives to create comprehensive training programs that equip professionals with the necessary skills to thrive in the field of data science. He has contributed to various online courses on platforms like Coursera, including topics such as AI workflows and machine learning model deployment. Dr. Lopez holds a Ph.D. in Experimental Physiological Psychology from the University of Texas at Arlington, where his dissertation explored critical thinking interventions in online learning environments. His multifaceted expertise positions him as a significant contributor to advancing data science education and practice within IBM and beyond.
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