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Snehan Kekre
Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time!
Read more
Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time!
Read more
Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time!
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.
Register for this course and see more details by visiting:
OpenCourser.com/course/mxfxxs/predict
What's inside
Syllabus
Project: Predict Employee Turnover with scikit-learn
Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a firm grasp of basic concepts required to make intelligent use of machine learning
Enhances understanding of machine learning concepts, which are applicable in various domains
Provides hands-on experience with a popular machine learning library, scikit-learn
Utilizes interactive Jupyter widgets for hands-on parameter adjustment and real-time visualization
Suitable for individuals with a basic understanding of Python and machine learning concepts
Save this course
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Reviews summary
Scikit-learn decision trees & random forest
Learners say this great course has many useful libraries and engaging assignments. It's well received and can be a solid course for beginners interested in the basics of machine learning. While it's not a replacement for advanced content, it is a good starting point.
Course has engaging assignments.
"Doing hands on project on Rhyme was very helpful as we could listen to the instructions and learn and type it ourselves."
"I was looking for Elaborated explanation of the project and implement it to clear the concept.This course did explain it all."
"very useful project, really enjoyed while doing! "
Suitable for those new to ML.
"Overall Good Experience"
"Just right for the basics of Machine Learning"
"The Course was very productive ."
Course uses beneficial libraries.
"I came across some unknown features of Pandas (profile), sklearn library. New python libraries like yellowbrick."
"I was hoping to learn a bit more advanced stuff but picked up some useful libraries that I never used it before."
"Get to learn something new. Like I have not used the interactive dashboard when creating the model. Also get to know about some very useful libraries that I was not using before and I used them more often."
Outdated libraries, typographical errors.
"This consumes a lot of time searching them over the internet."
"Also, some of the python libraries that were used are deprecated and are not running on our notebooks."
"the course was designed well and easy to follow. I was hoping to learn a bit more advanced stuff but picked up some useful libraries that I never used it before. Just watch out for little typo when you named a dataset as "data" and next section of the video you called it "hr"."
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 Predict Employee Turnover with scikit-learn with these
activities:
Review Your Notes and Course Materials on Decision Trees and Random Forests
Show steps
Refresh your memory and strengthen your foundational understanding
Browse courses on
Decision Trees
Show steps
-
Go through your notes and course materials
-
Identify areas where you need additional clarification
-
Review the relevant sections or seek additional resources
Review 'Machine Learning with scikit-learn' by Sebastian Raschka
Show steps
Provide foundational context on scikit-learn and machine learning
View
Machine Learning Q and AI: 30 Essential...
on Amazon
Show steps
-
Read and understand key concepts of scikit-learn library
-
Identify and summarize the core algorithms and techniques in machine learning
-
Create a summary of hands-on examples provided in the book
Create a Collection of Decision Tree and Random Forest Resources
Show steps
Deepen your understanding and expand your knowledge base
Browse courses on
Decision Trees
Show steps
-
Gather articles, tutorials, and other resources on decision trees and random forests
-
Organize and categorize the resources
-
Share your compilation with classmates or other learners
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in Peer Discussion on Decision Tree and Random Forest Applications
Show steps
Gain diverse perspectives and enhance your learning
Browse courses on
Decision Trees
Show steps
-
Join or organize a peer discussion group
-
Discuss different applications of decision trees and random forests
-
Share your insights and experiences
Follow Tutorials on Advanced Decision Tree and Random Forest Techniques
Show steps
Enhance your skills and learn more advanced techniques
Browse courses on
Decision Trees
Show steps
-
Identify tutorials on advanced decision tree and random forest techniques
-
Complete the tutorials and practice the techniques
-
Apply the techniques to practical examples
Practice Decision Tree and Random Forest Exercises
Show steps
Solidify understanding of how to use decision trees and random forests
Browse courses on
Decision Trees
Show steps
-
Complete practice exercises on constructing decision trees
-
Complete practice exercises on building random forest models
-
Submit your solutions and receive feedback on your approach
Attend a Meetup or Conference on Machine Learning and Data Science
Show steps
Connect with professionals and learn about industry trends
Browse courses on
Machine Learning
Show steps
-
Identify and attend a relevant Meetup or conference
-
Network with other attendees and speakers
-
Attend presentations and workshops on decision trees and random forests
Apply Decision Trees and Random Forests to a Real-World Dataset
Show steps
Gain practical experience in applying decision trees and random forests
Browse courses on
Machine Learning
Show steps
-
Identify a suitable dataset for your project
-
Clean and prepare the dataset
-
Develop decision tree and random forest models
-
Evaluate and compare the performance of your models
-
Create a report summarizing your findings
Review Your Notes and Course Materials on Decision Trees and Random Forests
Show steps
Refresh your memory and strengthen your foundational understanding
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Go through your notes and course materials
- Identify areas where you need additional clarification
- Review the relevant sections or seek additional resources
Review 'Machine Learning with scikit-learn' by Sebastian Raschka
Show steps
Provide foundational context on scikit-learn and machine learning
View
Machine Learning Q and AI: 30 Essential...
on Amazon
Show steps
Show steps
Show steps
- Read and understand key concepts of scikit-learn library
- Identify and summarize the core algorithms and techniques in machine learning
- Create a summary of hands-on examples provided in the book
Create a Collection of Decision Tree and Random Forest Resources
Show steps
Deepen your understanding and expand your knowledge base
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Gather articles, tutorials, and other resources on decision trees and random forests
- Organize and categorize the resources
- Share your compilation with classmates or other learners
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in Peer Discussion on Decision Tree and Random Forest Applications
Show steps
Gain diverse perspectives and enhance your learning
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Join or organize a peer discussion group
- Discuss different applications of decision trees and random forests
- Share your insights and experiences
Follow Tutorials on Advanced Decision Tree and Random Forest Techniques
Show steps
Enhance your skills and learn more advanced techniques
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Identify tutorials on advanced decision tree and random forest techniques
- Complete the tutorials and practice the techniques
- Apply the techniques to practical examples
Practice Decision Tree and Random Forest Exercises
Show steps
Solidify understanding of how to use decision trees and random forests
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Complete practice exercises on constructing decision trees
- Complete practice exercises on building random forest models
- Submit your solutions and receive feedback on your approach
Attend a Meetup or Conference on Machine Learning and Data Science
Show steps
Connect with professionals and learn about industry trends
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and attend a relevant Meetup or conference
- Network with other attendees and speakers
- Attend presentations and workshops on decision trees and random forests
Apply Decision Trees and Random Forests to a Real-World Dataset
Show steps
Gain practical experience in applying decision trees and random forests
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable dataset for your project
- Clean and prepare the dataset
- Develop decision tree and random forest models
- Evaluate and compare the performance of your models
- Create a report summarizing your findings
Career center
Learners who complete Predict Employee Turnover with scikit-learn will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and implementing machine learning models to solve real-world problems. This course provides you with the foundation you need to build a successful career as a Machine Learning Engineer. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to identify patterns in data and make predictions. With this knowledge, you can become a successful Machine Learning Engineer and help businesses make better decisions.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, interprets, and presents data to help businesses make better decisions. This course teaches you the essential skills of decision trees and random forests, which can help you extract insights from data and communicate your findings effectively. With this knowledge, you can make a significant contribution to any organization as a Data Analyst.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course provides you with the skills necessary to learn scikit-learn, a machine learning library in Python. With this knowledge, you can become a Data Scientist and make informed decisions using data.
Business Analyst
Business Analyst
A Business Analyst identifies and solves business problems by analyzing data and making recommendations. This course provides you with the analytical skills needed to succeed as a Business Analyst. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to identify trends and patterns in data. With this knowledge, you can make recommendations that will help your organization achieve its goals.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical modeling to solve complex problems in finance and other industries. This course teaches you the essential skills of decision trees and random forests, which can help you build accurate financial models. With this knowledge, you can make a significant contribution to any financial institution as a Quantitative Analyst.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems in business and industry. This course provides you with the skills needed to succeed as an Operations Research Analyst. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to optimize processes and make better decisions.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and mitigates risks for businesses and other organizations. This course provides you with the analytical skills needed to succeed as a Risk Analyst. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to identify and quantify risks.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess and manage risks in the insurance and financial services industries. This course teaches you the essential skills of decision trees and random forests, which can help you build accurate actuarial models. With this knowledge, you can make a significant contribution to any insurance or financial services company as an Actuary.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data to help businesses and other organizations make better decisions. This course provides you with the analytical skills needed to succeed as a Statistician. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to identify trends and patterns in data.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This course provides you with the skills needed to succeed as a Data Engineer. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to build data pipelines that can handle large volumes of data.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides you with the skills needed to succeed as a Software Engineer. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to build software systems that can handle large volumes of data.
Computer Scientist
Computer Scientist
A Computer Scientist conducts research and develops new computing technologies. This course provides you with the skills needed to succeed as a Computer Scientist. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to develop new computing technologies.
Information Scientist
Information Scientist
An Information Scientist collects, analyzes, and interprets data to help businesses and other organizations make better decisions. This course provides you with the skills needed to succeed as an Information Scientist. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to identify trends and patterns in data.
Data Architect
Data Architect
A Data Architect designs and builds data warehouses and other data management systems. This course provides you with the skills needed to succeed as a Data Architect. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to build data management systems that can handle large volumes of data.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course provides you with the skills needed to succeed as a Database Administrator. It covers the fundamentals of decision trees and random forests, and you'll learn how to use these algorithms to optimize database performance.
For more career information including salaries, visit:
OpenCourser.com/course/mxfxxs/predict
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
Predict Employee Turnover with scikit-learn.
Provides a comprehensive overview of decision tree algorithms, including their strengths and weaknesses. It also covers advanced topics such as ensemble methods and feature selection.
Practical guide to using scikit-learn, a popular Python library for machine learning. It covers a wide range of topics, from data preprocessing to model evaluation.
Comprehensive guide to machine learning with Python. It covers a wide range of topics, from supervised learning to unsupervised learning.
Gentle introduction to machine learning with Python. It covers the basics of machine learning, including data preprocessing, model training, and model evaluation.
Practical guide to using machine learning for real-world problems. It covers a wide range of topics, from the basics of machine learning to the latest advances in the field.
Save
Gentle introduction to machine learning with Python. It covers the basics of machine learning, including data preprocessing, model training, and model evaluation.
Save
Comprehensive overview of deep learning, a powerful technique for machine learning. It covers a wide range of topics, from the basics of deep learning to the latest advances in the field.
Comprehensive overview of reinforcement learning, a powerful technique for machine learning. It covers a wide range of topics, from the basics of reinforcement learning to the latest advances in the field.
Comprehensive guide to machine learning with R, a popular programming language for data analysis. It covers a wide range of topics, from the basics of machine learning to the latest advances in the field.
Short, practical guide to machine learning. It covers the basics of machine learning, including data preprocessing, model training, and model evaluation.
Comprehensive overview of natural language processing, a powerful technique for machine learning. It covers a wide range of topics, from the basics of natural language processing to the latest advances in the field.
Practical guide to using machine learning for hacking. It covers a wide range of topics, from the basics of machine learning to the latest advances in the field.
Save
Comprehensive overview of computer vision, a powerful technique for machine learning. It covers a wide range of topics, from the basics of computer vision to the latest advances in the field.
Comprehensive overview of speech and language processing, a powerful technique for machine learning. It covers a wide range of topics, from the basics of speech and language processing to the latest advances in the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mxfxxs/predict
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Builds a firm grasp of basic concepts required to make intelligent use of machine learning
Enhances understanding of machine learning concepts, which are applicable in various domains
Provides hands-on experience with a popular machine learning library, scikit-learn
Utilizes interactive Jupyter widgets for hands-on parameter adjustment and real-time visualization
Suitable for individuals with a basic understanding of Python and machine learning concepts
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