We may earn an affiliate commission when you visit our partners.
Mark J Grover
and
Ray Lopez, Ph.D.
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/1n1994/ai
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
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.
Read more
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.
Read more
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
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
Save this course
Save AI Workflow: Feature Engineering and Bias Detection to your list so you can find it easily later:
Save
Save
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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.
Browse courses on
Feature Engineering
Show steps
-
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
Expand to see all activities and additional details
Show all 11 activities
Practice detecting bias in machine learning models
Show steps
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
Show steps
-
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
Show steps
Understanding bias mitigation techniques will equip you with the knowledge to develop fairer and more accurate machine learning models.
Browse courses on
Bias Mitigation
Show steps
-
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
Show steps
Create a classification model to address class imbalances, showcasing your understanding of handling imbalanced data in real-world scenarios.
Show steps
-
Load and explore imbalanced dataset
-
Apply data sampling techniques to balance classes
-
Train and evaluate classification models
Discuss topic modeling techniques
Show steps
Participating in discussions on topic modeling techniques will allow you to exchange ideas with others and deepen your understanding.
Browse courses on
Topic Modeling
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Practicing clustering algorithms will strengthen your ability to group data points into meaningful clusters.
Browse courses on
Clustering
Show steps
-
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
Show steps
Creating an infographic on dimension reduction techniques will help you solidify your understanding of the concepts and their applications.
Browse courses on
Dimension Reduction
Show steps
-
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
Show steps
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
Show steps
-
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
Review probability theory
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all 11 activities
Practice detecting bias in machine learning models
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Understanding bias mitigation techniques will equip you with the knowledge to develop fairer and more accurate machine learning models.
Browse courses on
Bias Mitigation
Show steps
Show steps
Show steps
- 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
Show steps
Create a classification model to address class imbalances, showcasing your understanding of handling imbalanced data in real-world scenarios.
Show steps
Show steps
Show steps
- Load and explore imbalanced dataset
- Apply data sampling techniques to balance classes
- Train and evaluate classification models
Discuss topic modeling techniques
Show steps
Participating in discussions on topic modeling techniques will allow you to exchange ideas with others and deepen your understanding.
Browse courses on
Topic Modeling
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Practicing clustering algorithms will strengthen your ability to group data points into meaningful clusters.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- 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
Show steps
Creating an infographic on dimension reduction techniques will help you solidify your understanding of the concepts and their applications.
Browse courses on
Dimension Reduction
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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 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.
Machine Learning Engineer
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.
Data Analyst
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.
Software Engineer
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.
Business Analyst
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.
Product Manager
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.
Quantitative Analyst
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.
Statistician
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.
Business Intelligence Analyst
Business Intelligence Analyst
Business intelligence analysts use data analysis techniques to identify trends and patterns that can help businesses make informed decisions.
Data Architect
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.
Data Engineer
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.
Information Systems Manager
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.
Database Administrator
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.
Risk Analyst
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.
Operations Research Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/1n1994/ai
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AI Workflow: Feature Engineering and Bias Detection.
Provides a comprehensive overview of statistical learning methods, with a focus on theoretical foundations and practical applications.
Save
Provides a comprehensive overview of data mining techniques, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of pattern recognition and machine learning techniques, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of feature engineering techniques, with a focus on practical applications and real-world examples.
Provides a practical guide to building and deploying machine learning models using popular Python libraries, with a focus on feature engineering and model evaluation.
Provides a comprehensive overview of unsupervised learning techniques, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of clustering algorithms, with a focus on theoretical foundations and practical applications.
Provides a comprehensive overview of dimensionality reduction techniques, with a focus on practical applications and real-world examples.
Provides a comprehensive overview of outlier detection techniques, with a focus on practical applications and real-world examples.
Provides a more accessible introduction to statistical learning methods, with a focus on practical applications and real-world examples.
Provides a comprehensive overview of machine learning techniques using the R programming language, with a focus on practical applications and real-world examples.
Provides a practical introduction to machine learning for data science, with a focus on real-world applications and case studies.
Provides a comprehensive overview of data analysis techniques using the Python programming language, with a focus on practical applications and real-world examples.
Provides a business-oriented perspective on data science, including best practices for data collection, analysis, and visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1n1994/ai
Share
Similar courses
Here are nine courses similar to
AI Workflow: Feature Engineering and Bias Detection.
Preparing Data for Modeling with scikit-learn
OpenCourser.com/course/zikm2d/preparing
Preparing Data for Modeling with scikit-learn
Coping with Missing, Invalid, and Duplicate Data in R
OpenCourser.com/course/txqm5b/coping
Coping with Missing, Invalid, and Duplicate Data in R
AI Workflow: AI in Production
OpenCourser.com/course/hbv0w2/ai
AI Workflow: AI in Production
AI Workflow: Data Analysis and Hypothesis Testing
OpenCourser.com/course/sl0k0b/ai
AI Workflow: Data Analysis and Hypothesis Testing
Data Analysis with Python Project
OpenCourser.com/course/b3nagq/data
Data Analysis with Python Project
Association Rules Analysis
OpenCourser.com/course/h7xvvk/association
Association Rules Analysis
AI Workflow: Enterprise Model Deployment
OpenCourser.com/course/8vka1p/ai
AI Workflow: Enterprise Model Deployment
Responsible AI for Developers: Fairness & Bias
OpenCourser.com/course/vup343/responsible
Responsible AI for Developers: Fairness & Bias
Performing Threat Modeling with the PASTA Methodology
OpenCourser.com/course/hiuxmu/performing
Performing Threat Modeling with the PASTA Methodology
Effort
This course requires 7.5 to 9 hours of study.
Level
Advanced
Via
Coursera
Institution
IBM
Instructors
Mark J Grover
Ray Lopez, Ph.D.
Language
English
This course belongs to a
collection
called
IBM AI Enterprise Workflow. It's comprised of six courses:
- AI Workflow: Enterprise Model Deployment
- AI Workflow: Business Priorities and Data Ingestion
- AI Workflow: Feature Engineering and Bias Detection (viewing)
- AI Workflow: Machine Learning, Visual Recognition and NLP
- AI Workflow: AI in Production
- AI Workflow: Data Analysis and Hypothesis Testing
Good to know
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
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser