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AI Workflow: Feature Engineering and Bias Detection

Approx. 12 hours to complete

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Course Summary

This course provides an introduction to feature engineering and bias detection in AI workflows using IBM Watson Studio. Students will learn key concepts and techniques for data preparation, feature selection, and model evaluation, as well as strategies for detecting and mitigating bias in machine learning models.

Key Learning Points

Learn to prepare data for machine learning models
Discover key feature engineering techniques
Explore strategies for detecting and mitigating bias in AI
Gain hands-on experience with IBM Watson Studio
Collaborate with peers on real-world projects

Learning Outcomes

Develop skills in data preparation and feature engineering
Understand strategies for detecting and mitigating bias in AI workflows
Gain hands-on experience with IBM Watson Studio

Prerequisites or good to have knowledge before taking this course

Basic understanding of machine learning concepts
Prior experience with Python programming

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Project-based

Similar Courses

IBM AI Engineering Professional Certificate
Applied Data Science with Python Specialization

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Notable People in This Field

Professor of Computer Science, Stanford University
Founder, deeplearning.ai

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Description

This is the third course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

Course 3 introduces you to the next stage of the workflow for our hypothetical media company. In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data. 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 Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 and 2 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

Outline

Data transforms and feature engineering
Data Transformations Overview
Introduction to Class Imbalance
Class Imbalance Deep Dive
Introduction to Dimensionality Reduction
Dimension Reduction
Case Study Intro / Feature Engineering
Data Transformation: Through the eyes of our Working Example
Transforms with scikit-learn
Pipelines
Class imbalance: Through the Eyes of our Working Example
Class Imbalance
Sampling Techniques
Models that Naturally Handle Imbalance
Data Bias
Dimensionality Reduction: Through the Eyes of Our Working Example
Why is Dimensionality Reduction Important?
Dimensionality Reduction and Topic models
Topic modeling: Through the Eyes of our Working Example
Getting Started with the Topic Modeling Case Study (hands-on)
Data Transforms and Feature Engineering: Summary/Review
Getting Started: Check for Understanding
Class Imbalance, Data Bias: Check for Understanding
Dimensionality Reduction: Check for Understanding
CASE STUDY - Topic Modeling: Check for Understanding
Data Transforms and Feature Engineering: End of Module Quiz

Pattern recognition and data mining best practices
Exploring IBM's AI Fairness 360 Toolkit
Introduction to Outliers
Outlier Detection
Introduction to Unsupervised learning
Unsupervised Learning
ai360: Through the Eyes of our Working Example
Introduction to 360 (hands-on)
Outlier Detection: Through the Eyes of our Working Example
Outliers
Unsupervised learning: Through the Eyes of our Working Example
An Overview of Unsupervised Learning
Clustering
Clustering Evaluation
Clustering: Through the Eyes of our Working Example
Getting Started with the Clustering Case Study (hands-on)
Pattern Recognition and Data Mining Best Practices: Summary/Review
ai360 Tutorial: Check for Understanding
Outlier Detection: Check for Understanding
Unsupervised Learning: Check for Understanding
CASE STUDY - Clustering: Check for Understanding
Pattern Recognition and Data Mining Best Practices: End of Module Quiz

Summary of User Reviews

Discover the IBM AI Workflow: Feature Engineering and Bias Detection course on Coursera. Learn how to develop and implement machine learning models in a responsible and ethical manner. Users have praised the course for its comprehensive coverage of AI workflows and its practical applications.

Key Aspect Users Liked About This Course

Comprehensive coverage of AI workflows and practical applications

Pros from User Reviews

Clear explanations of complex concepts
Extensive hands-on exercises and real-world case studies
Practical tips for detecting and mitigating bias in machine learning models

Cons from User Reviews

Some users found the course too technical and difficult to follow
The course could benefit from more interactive elements and quizzes
The pace of the course may be too slow for experienced data scientists

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