AI Workflow: Feature Engineering and Bias Detection

AI Workflow: Feature Engineering and Bias Detection

AI Workflow: Feature Engineering and Bias Detection This course is part of IBM AI Enterprise Workflow Specialization Taught in English Instructors: Mark J Grover +1 more Close Instructors Instructor ratings We asked all learners to give feedback on our instructors based on the quality of their teaching style. Close 3.9 (12 ratings) Mark J Grover

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Description

Artificial Intelligence (AI) has become an increasingly integral part of many industries, playing a crucial role in decision-making processes and automation. However, the success of AI models heavily relies on the input data and the workflow that governs how this data is processed and utilized. In recent years, a growing emphasis has been placed on feature engineering and bias detection within AI workflows to ensure the accuracy and fairness of AI models.

Feature engineering is the process of selecting, transforming, and creating input variables that are used to train machine learning models. It involves identifying relevant features in the data that can help improve the performance of the AI model. By carefully selecting and shaping input variables, feature engineering can greatly enhance the predictive power of AI models, leading to more accurate and reliable outcomes.

Moreover, feature engineering

AI Workflow: Feature Engineering and Bias Detection

This course is part of IBM AI Enterprise Workflow Specialization

Taught in English

Mark J Grover
Ray Lopez, Ph.D.

Instructors: Mark J Grover

3,968 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.3

(67 reviews)

Advanced level
Designed for those already in the industry
12 hours (approximately)
Flexible schedule
Learn at your own pace

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10 quizzes

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