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AI Workflow: Feature Engineering and Bias Detection
  • Level Expert
  • Duration 12 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Getting Started

    • 1 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Getting Started: Check for Understanding

1 Videos

  • Data Transformations Overview

3 Readings

  • Data Transformation: Through the eyes of our Working Example
  • Transforms with scikit-learn
  • Pipelines

Class imbalance, data bias

    • 2 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Class Imbalance, Data Bias: Check for Understanding

2 Videos

  • Introduction to Class Imbalance
  • Class Imbalance Deep Dive

5 Readings

  • Class imbalance: Through the Eyes of our Working Example
  • Class Imbalance
  • Sampling Techniques
  • Models that Naturally Handle Imbalance
  • Data Bias

Dimensionality Reduction

    • 2 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Dimensionality Reduction: Check for Understanding

2 Videos

  • Introduction to Dimensionality Reduction
  • Dimension Reduction

3 Readings

  • Dimensionality Reduction: Through the Eyes of Our Working Example
  • Why is Dimensionality Reduction Important?
  • Dimensionality Reduction and Topic models

CASE STUDY - Topic modeling

    • 1 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • CASE STUDY - Topic Modeling: Check for Understanding

1 Labs

  • Case Study Answer Key Notebook

1 Videos

  • Case Study Intro / Feature Engineering

2 Readings

  • Topic modeling: Through the Eyes of our Working Example
  • Getting Started with the Topic Modeling Case Study (hands-on)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Transforms and Feature Engineering: End of Module Quiz

1 Readings

  • Data Transforms and Feature Engineering: Summary/Review

TUTORIAL: ai360

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • ai360 Tutorial: Check for Understanding

1 Videos

  • Exploring IBM's AI Fairness 360 Toolkit

2 Readings

  • ai360: Through the Eyes of our Working Example
  • Introduction to 360 (hands-on)

Outlier detection

    • 2 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Outlier Detection: Check for Understanding

2 Videos

  • Introduction to Outliers
  • Outlier Detection

2 Readings

  • Outlier Detection: Through the Eyes of our Working Example
  • Outliers

Unsupervised learning

    • 2 Videos
    • 4 Readings
    • 1 Assignment
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1 Assignment

  • Unsupervised Learning: Check for Understanding

2 Videos

  • Introduction to Unsupervised learning
  • Unsupervised Learning

4 Readings

  • Unsupervised learning: Through the Eyes of our Working Example
  • An Overview of Unsupervised Learning
  • Clustering
  • Clustering Evaluation

CASE STUDY - Clustering

    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • CASE STUDY - Clustering: Check for Understanding

1 Labs

  • Case Study Answer Key Notebook

2 Readings

  • Clustering: Through the Eyes of our Working Example
  • Getting Started with the Clustering Case Study (hands-on)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Pattern Recognition and Data Mining Best Practices: End of Module Quiz

1 Readings

  • Pattern Recognition and Data Mining Best Practices: Summary/Review

Auto Summary

Enhance your data science expertise with "AI Workflow: Feature Engineering and Bias Detection," part of the IBM AI Enterprise Workflow Certification. Led by IBM experts, this advanced course delves into feature engineering, class imbalance handling, and bias detection. It covers dimension reduction, outlier detection, and unsupervised learning techniques. Ideal for seasoned data science practitioners, the 720-minute course offers hands-on experience with AI Fairness 360, topic modeling, and visualization. Available through Coursera with Starter and Professional subscription options.

Mark J Grover
Instructors

Mark J Grover

Ray Lopez, Ph.D.
Instructors

Ray Lopez, Ph.D.