AI Workflow: Feature Engineering and Bias Detection
Master advanced feature engineering techniques, bias detection, and unsupervised learning methods for enterprise AI workflows.
Master advanced feature engineering techniques, bias detection, and unsupervised learning methods for enterprise AI workflows.
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.
4.4
(68 ratings)
4,717 already enrolled
Instructors:
English
3 languages available
Powered by
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
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.
Expert in Data Science and AI Education
Ray Lopez, Ph.D., is a prominent technical and educational expert with over 30 years of experience in various fields, including software development, system administration, and technical architecture. He has a strong background in basic research in neuroscience and artificial intelligence, which complements his extensive teaching experience at the university level in subjects such as science, mathematics, statistics, and philosophy. Currently serving as the Data Science Curriculum Leader at IBM, Dr. Lopez is dedicated to developing education and certification programs that enhance skills in data science.His current projects focus on creating comprehensive courses that cover critical aspects of AI workflows, including data analysis, machine learning, and enterprise model deployment. Dr. Lopez's work aims to bridge the gap between business priorities and technical implementations, equipping learners with the necessary tools to succeed in the evolving landscape of data science and AI technologies.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.4 course rating
68 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.