AI Workflow: Feature Engineering and Bias Detection from Coursera

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.

What's inside

Syllabus

Data transforms and feature engineering

This module will introduce you to skills required for effective feature engineering in today's business enterprises. The skills are presented as a series of best practices representing years of practical experience.

Pattern recognition and data mining best practices

This module will continue the discussion of skill related to feature engineering for practicing data scientists, with a focus on outliers and the use of unsupervised learning techniques for finding patterns.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in feature engineering, which is a core skill for the data science profession

Emphasizes ethical considerations in building and deploying AI, which is crucial for responsible AI practices

Introduces advanced machine learning techniques like bias detection and dimension reduction, which are essential for building robust and accurate models

Employs the popular and open-source AI Fairness 360 library for bias detection, which is widely used in industry

Requires prerequisite knowledge in machine learning and data science, which may not be suitable for beginners

Reviews summary

Feature engineering fundamentals course

Learners say this basic course provides a solid foundation and aids in developing a logical methodology for feature engineering. While engaging assignments led to improvement, many felt the content lacked the depth expected from an advanced course.

Students experienced noticeable growth.

"It's quite good...as an 'advance' course."

Engaging assignments requiring logical thinking.

"engaging assignments"

Course offers a solid grounding in feature engineering.

"Basic course...solid foundation"

Content could be more in-depth.

"It's quite good but the content could be more in-depth..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Workflow: Feature Engineering and Bias Detection with these activities:

Review probability theory

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Reviewing probability theory will strengthen your foundational understanding of statistics, which is essential for understanding the concepts covered in this course.

Browse courses on Probability

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Read chapters 1-3 of a probability theory textbook
Solve practice problems from the textbook
Take an online quiz on probability theory

Learn about feature engineering best practices

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Explore a guided tutorial on feature engineering to develop a robust understanding of the fundamental concepts and best practices utilized in the field.

Browse courses on Feature Engineering

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Identify data sources and explore data
Apply feature engineering techniques to transform and enhance data
Evaluate feature importance and select relevant features

Practice feature engineering techniques

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By practicing feature engineering techniques, you will develop the skills necessary to transform raw data into features that can be used by machine learning models.

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Use a Python library such as scikit-learn or pandas to explore a dataset
Identify potential features and create them using feature engineering techniques
Evaluate the effectiveness of your features using metrics such as accuracy or F1 score

Eight other activities

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Practice detecting bias in machine learning models

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Engage in practice drills to enhance your ability to detect and mitigate bias in machine learning models, ensuring fairness and accuracy in your data-driven solutions.

Browse courses on Bias Mitigation

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Learn about different types of bias in ML models
Use AI Fairness 360 library to detect bias in models
Apply bias mitigation techniques to improve model fairness

Learn about bias mitigation techniques

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Understanding bias mitigation techniques will equip you with the knowledge to develop fairer and more accurate machine learning models.

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Read articles or watch videos on bias mitigation techniques
Follow tutorials on implementing bias mitigation techniques in Python
Apply bias mitigation techniques to a real-world dataset

Build a classification model to mitigate class imbalances

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Create a classification model to address class imbalances, showcasing your understanding of handling imbalanced data in real-world scenarios.

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Load and explore imbalanced dataset
Apply data sampling techniques to balance classes
Train and evaluate classification models

Discuss topic modeling techniques

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Participating in discussions on topic modeling techniques will allow you to exchange ideas with others and deepen your understanding.

Browse courses on Topic Modeling

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Join an online forum or discussion group dedicated to topic modeling
Participate in discussions and ask questions
Share your own insights and knowledge

Learn about outlier detection algorithms

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Understanding outlier detection algorithms will enable you to identify and handle outliers in your data, improving the accuracy of your machine learning models.

Browse courses on Outlier Detection

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Read articles or watch videos on outlier detection algorithms
Follow tutorials on implementing outlier detection algorithms in Python
Apply outlier detection algorithms to a real-world dataset

Practice clustering algorithms

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Practicing clustering algorithms will strengthen your ability to group data points into meaningful clusters.

Browse courses on Clustering

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Use a Python library such as scikit-learn to explore a dataset
Apply different clustering algorithms to the dataset
Evaluate the effectiveness of your clustering algorithms using metrics such as silhouette score or Davies-Bouldin index

Develop an infographic on dimension reduction techniques

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Creating an infographic on dimension reduction techniques will help you solidify your understanding of the concepts and their applications.

Browse courses on Dimension Reduction

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Research different dimension reduction techniques
Design an infographic that explains the techniques in a clear and concise way
Share your infographic with others

Design and implement a data visualization dashboard

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Creating a data visualization dashboard will help you develop your skills in presenting data in a clear and actionable way.

Browse courses on Data Visualization

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Choose a dataset to visualize
Design the dashboard, including the charts and graphs you will use
Implement the dashboard using a data visualization tool such as Tableau or Power BI
Share your dashboard with others

Career center

Learners who complete AI Workflow: Feature Engineering and Bias Detection will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists analyze data, develop algorithms, and build models to extract meaningful insights from data. Machine learning and feature engineering are at the core of a data scientist's work. By completing this course, you will be better equipped with skills and knowledge that data scientists commonly use. Learning about detecting bias in data is especially important for data scientists, as bias can skew results.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers take data science models and prepare them for deployment in products and services. This course directly teaches skills that are foundational to the work of ML engineers, like feature engineering.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data analysts use data and statistical techniques to identify trends and patterns. Feature engineering is a core skill for data analysts, as it allows them to transform raw data into more useful and informative features. This course provides a comprehensive overview of feature engineering best practices.

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Software Engineer

Software engineers design, develop, and maintain software applications. While not directly related to the field of data science, this course may be useful for software engineers who want to learn more about feature engineering and data analysis techniques.

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Business Analyst

Business analysts help organizations understand and improve their business processes. By completing this course, business analysts can gain a better understanding of data analysis techniques and how they can be used to improve decision-making.

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Product Manager

Product managers are responsible for defining, developing, and launching new products and features. This course may be useful for product managers who want to learn more about feature engineering and data analysis techniques.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze financial data. This course may be useful for quantitative analysts who want to learn more about feature engineering and data analysis techniques.

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Statistician

Statisticians collect, analyze, and interpret data. This course may be useful for statisticians who want to learn more about feature engineering and data analysis techniques.

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Business Intelligence Analyst

Business intelligence analysts use data analysis techniques to identify trends and patterns that can help businesses make informed decisions.

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Data Architect

Data architects design and manage the architecture of data systems. This course may be useful for data architects who want to learn more about feature engineering and data analysis techniques.

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Data Engineer

Data engineers build and maintain the infrastructure that supports data analysis and machine learning. This course may be useful for data engineers who want to learn more about feature engineering and data analysis techniques.

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Information Systems Manager

Information systems managers oversee the management of information systems. This course may be useful for information systems managers who want to learn more about feature engineering and data analysis techniques.

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Database Administrator

Database administrators manage and maintain databases. This course may be useful for database administrators who want to learn more about feature engineering and data analysis techniques.

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Risk Analyst

Risk analysts use data and statistical techniques to assess risk. This course may be useful for risk analysts who want to learn more about feature engineering and data analysis techniques.

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Operations Research Analyst

Operations research analysts use mathematical and statistical models to solve business problems. This course may be useful for operations research analysts who want to learn more about feature engineering and data analysis techniques.

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