Apply decision trees and random forests with scikit-learn to classification problems
Interpret decision trees and random forest models using feature importances
Tune model hyperparamters to improve classification accuracy
Create interactive, GUI components in Jupyter notebooks using widgets
Showcase this hands-on experience in an interview
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.
Some experience in Python programming and machine learning theory is recommended.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Importing Libraries
Exploratory Data Analysis
Encode Categorical Features
Visualize Class Imbalance
Create Training and Test Sets
Build a Decision Tree Classifier with Interactive Controls
Build a Decision Tree Classifier with Interactive Controls (Continued)
Build a Random Forest Classifier with Interactive Controls
Feature Importance and Evaluation Metrics
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Good practical overview of decision tree and random forest model with Python. The interface for code typing is a bit difficult to navigate with some lag time; hence -1 star in the review.
I am glad to have taken this course. I came across some unknown features of Pandas (profile), sklearn library. New python libraries like yellowbrick.
I was looking for Elaborated explanation of the project and implement it to clear the concept. This course did explain it all.
Doing hands on project on Rhyme was very helpful as we could listen to the instructions and learn and type it ourselves.
Are Guided Projects available on desktop and mobile?
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Who are the instructors for Guided Projects?
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.
Can I download the work from my Guided Project after I complete it?
You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.
How much experience do I need to do this Guided Project?
At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Can I complete this Guided Project right through my web browser, instead of installing special software?
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
What is the learning experience like with Guided Projects?
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
More questions? Visit the Learner Help Center.