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Ryan Ahmed
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features.
Read more
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features.
Read more
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/7dbvqx/employee
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What's inside
Syllabus
Project Overview
In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Project-based learning
Practical application in Human Resources
Intermediate level
North America based only
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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 Employee Attrition Prediction Using Machine Learning with these
activities:
Review linear algebra and calculus concepts
Show steps
Strengthen the foundation by revisiting essential mathematical concepts, enhancing comprehension of the course materials.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of vectors, matrices, and linear transformations.
-
Practice solving calculus problems involving derivatives and integrals.
Review essential statistics textbooks
Show steps
Review statistical concepts and learning from textbooks to establish a strong foundation for the course material.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read through the chapters on probability distributions, hypothesis testing, and linear regression.
-
Work through the practice problems at the end of each chapter.
-
Complete the online quizzes and assignments associated with the textbooks.
Compile a glossary of machine learning terms and concepts
Show steps
Enhance vocabulary and understanding by creating a comprehensive glossary, solidifying knowledge of machine learning terms and concepts.
Show steps
-
Review the course materials and identify key terms.
-
Research and define each term clearly and concisely.
-
Organize the terms alphabetically or by category.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice exercises on logistic regression and gradient boosting
Show steps
Engage in hands-on practice to enhance understanding and proficiency in applying the machine learning techniques covered in the course.
Browse courses on
Logistic Regression
Show steps
-
Solve the practice exercises provided in the course materials or textbook.
-
Participate in online forums or discussion groups to discuss and clarify concepts.
-
Complete online coding challenges or competitions related to logistic regression and gradient boosting.
Develop a machine learning model for employee attrition prediction
Show steps
Apply the learned concepts to a practical scenario, fostering a deeper understanding of employee attrition factors and model development.
Show steps
-
Collect and preprocess the necessary employee data.
-
Train and evaluate the logistic regression and gradient boosting models.
-
Compare the performance of the models and select the best performing one.
-
Write a report summarizing the project findings and recommendations.
Mentor junior learners or colleagues on machine learning
Show steps
Reinforce understanding by explaining concepts to others, fostering a deeper grasp of machine learning and enhancing communication skills.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor junior learners or colleagues.
-
Provide guidance on machine learning concepts and techniques.
-
Review their work and provide constructive feedback.
-
Encourage them to ask questions and engage in discussions.
Review linear algebra and calculus concepts
Show steps
Strengthen the foundation by revisiting essential mathematical concepts, enhancing comprehension of the course materials.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and linear transformations.
- Practice solving calculus problems involving derivatives and integrals.
Review essential statistics textbooks
Show steps
Review statistical concepts and learning from textbooks to establish a strong foundation for the course material.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read through the chapters on probability distributions, hypothesis testing, and linear regression.
- Work through the practice problems at the end of each chapter.
- Complete the online quizzes and assignments associated with the textbooks.
Compile a glossary of machine learning terms and concepts
Show steps
Enhance vocabulary and understanding by creating a comprehensive glossary, solidifying knowledge of machine learning terms and concepts.
Show steps
Show steps
Show steps
- Review the course materials and identify key terms.
- Research and define each term clearly and concisely.
- Organize the terms alphabetically or by category.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice exercises on logistic regression and gradient boosting
Show steps
Engage in hands-on practice to enhance understanding and proficiency in applying the machine learning techniques covered in the course.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Solve the practice exercises provided in the course materials or textbook.
- Participate in online forums or discussion groups to discuss and clarify concepts.
- Complete online coding challenges or competitions related to logistic regression and gradient boosting.
Develop a machine learning model for employee attrition prediction
Show steps
Apply the learned concepts to a practical scenario, fostering a deeper understanding of employee attrition factors and model development.
Show steps
Show steps
Show steps
- Collect and preprocess the necessary employee data.
- Train and evaluate the logistic regression and gradient boosting models.
- Compare the performance of the models and select the best performing one.
- Write a report summarizing the project findings and recommendations.
Mentor junior learners or colleagues on machine learning
Show steps
Reinforce understanding by explaining concepts to others, fostering a deeper grasp of machine learning and enhancing communication skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners or colleagues.
- Provide guidance on machine learning concepts and techniques.
- Review their work and provide constructive feedback.
- Encourage them to ask questions and engage in discussions.
Career center
Learners who complete Employee Attrition Prediction Using Machine Learning will develop knowledge and skills
that may be useful to these careers:
Human Resources Analyst
Human Resources Analyst
Human Resources Analysts use data to make decisions about human resources policies and practices. This course can help you learn the basics of data analysis and how to apply it to problems in human resources. You will learn how to collect, clean, and analyze data, and how to communicate your findings to stakeholders. This knowledge will be valuable if you are interested in a career as a Human Resources Analyst.
Data Scientist
Data Scientist
Data Scientists apply machine learning and statistical modeling to very large datasets to uncover patterns and insights. This course can help you learn the basics of machine learning and how to apply it to problems in human resources. You will learn how to build and train models, evaluate their performance, and interpret the results. This knowledge will be valuable if you are interested in a career as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course can help you learn the basics of machine learning and how to apply it to problems in human resources. You will learn how to build and train models, evaluate their performance, and interpret the results. This knowledge will be valuable if you are interested in a career as a Machine Learning Engineer.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course can help you learn the basics of statistics and how to apply it to problems in human resources. You will learn about the different types of data, how to collect and clean data, and how to analyze data to make inferences. This knowledge will be valuable if you are interested in a career as a Statistician.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course can help you learn the basics of data analysis and how to apply it to problems in human resources. You will learn how to collect, clean, and analyze data, and how to communicate your findings to stakeholders. This knowledge will be valuable if you are interested in a career as a Data Analyst.
Human Resources Manager
Human Resources Manager
Human Resources Managers oversee all aspects of human resources, including recruiting, hiring, training, and development. This course can help you learn the basics of human resources management and how to apply it to problems in your organization. You will learn about the laws and regulations that govern human resources, and how to create and implement effective human resources policies and practices. This knowledge will be valuable if you are interested in a career as a Human Resources Manager.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. This course can help you learn the basics of actuarial science and how to apply it to problems in human resources. You will learn about the different types of risks, how to measure risk, and how to develop strategies to mitigate risk. This knowledge will be valuable if you are interested in a career as an Actuary.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect, analyze, and interpret data about markets. This course can help you learn the basics of market research and how to apply it to problems in human resources. You will learn about the different types of market research, how to conduct market research, and how to analyze market research data. This knowledge will be valuable if you are interested in a career as a Market Research Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematics and statistics to solve problems in business and industry. This course can help you learn the basics of operations research and how to apply it to problems in human resources. You will learn about the different types of operations research models, how to build and solve operations research models, and how to interpret the results of operations research models. This knowledge will be valuable if you are interested in a career as an Operations Research Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use data to make decisions about investments. This course can help you learn the basics of financial analysis and how to apply it to problems in human resources. You will learn about the different types of investments, how to evaluate investments, and how to make投资 decisions. This knowledge will be valuable if you are interested in a career as a Financial Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you learn the basics of software engineering and how to apply it to problems in human resources. You will learn about the different phases of the software development lifecycle, how to design and develop software systems, and how to test and debug software systems. This knowledge will be valuable if you are interested in a career as a Software Engineer.
IT Manager
IT Manager
IT Managers oversee all aspects of IT, including infrastructure, security, and operations. This course can help you learn the basics of IT management and how to apply it to problems in human resources. You will learn about the different types of IT systems, how to manage IT systems, and how to secure IT systems. This knowledge will be valuable if you are interested in a career as an IT Manager.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course can help you learn the basics of database administration and how to apply it to problems in human resources. You will learn about the different types of databases, how to design and implement databases, and how to maintain databases. This knowledge will be valuable if you are interested in a career as a Database Administrator.
Web Developer
Web Developer
Web Developers design and develop websites. This course can help you learn the basics of web development and how to apply it to problems in human resources. You will learn about the different types of websites, how to design and develop websites, and how to maintain websites. This knowledge will be valuable if you are interested in a career as a Web Developer.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. This course can help you learn the basics of business analysis and how to apply it to problems in human resources. You will learn about the different types of business analysis, how to conduct business analysis, and how to communicate the results of business analysis. This knowledge will be valuable if you are interested in a career as a Business Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/7dbvqx/employee
Reading list
We've selected nine 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
Employee Attrition Prediction Using Machine Learning.
Is an excellent resource for background knowledge on logistic regression, one of the algorithms used in the course.
An in-depth look at HR analytics for those who want a deeper understanding of the field.
Save
Provides an overview of machine learning and its applications in business, including HR.
Provides a hands-on approach to machine learning using the R programming language.
Explores the relationship between CSR and HRM, and how they can be used to improve employee engagement and retention.
An accessible introduction to machine learning for those with no prior knowledge.
A practical guide to deep learning using the Fastai and PyTorch libraries.
A classic textbook on statistical learning, which is the foundation of machine learning.
A comprehensive textbook on machine learning, covering a wide range of topics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7dbvqx/employee
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Project-based learning
Practical application in Human Resources
Intermediate level
North America based only
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