Classification Trees in Python, From Start To Finish
3.0 /5- Offered byCoursera
Classification Trees in Python, From Start To Finish at Coursera Overview
Classification Trees in Python, From Start To Finish
at Coursera
Learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding
Duration | 2 hours |
Total fee | ₹729 |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Credential | Certificate |
Classification Trees in Python, From Start To Finish at Coursera Highlights
Classification Trees in Python, From Start To Finish
at Coursera
- Students can download for free their created material
- Ability to access cloud desktop across six different sessions
- Get an instant access to the necessary software packages through Rhyme
- Get all learning materials, including the interactive workspace and final quiz
- Students will get split-screen video walkthrough of each step, from a subject-matter expert
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Classification Trees in Python, From Start To Finish at Coursera Course details
Classification Trees in Python, From Start To Finish
at Coursera
Skills you will learn
Who should do this course?
- For learners who are based in the North America region
What are the course deliverables?
- Create Classification Trees in Python
- Apply Cost Complexity Pruning in Python
- Apply Cross Validation in Python
- Create Confusion Matrices in Python
More about this course
- This course runs on Coursera's hands-on project platform called Rhyme
- On Rhyme, students do projects in a hands-on manner in their browser
- 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 Internet browser so you can just focus on learning
- For this project, students will get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed
Classification Trees in Python, From Start To Finish at Coursera Curriculum
Classification Trees in Python, From Start To Finish
at Coursera
Task 1: Import the modules that will do all the work
Task 2: Import the data
Task 3: Missing Data Part 1: Identifying Missing Data
Task 4: Missing Data Part 2: Dealing With Missing Data
Task 5: Format Data Part 1: Split the Data into Dependent and Independent Variables
Task 6: Format the Data Part 2: One-Hot Encoding
Task 7: Build A Preliminary Classification Tree
Classification Trees in Python, From Start To Finish at Coursera Faculty details
Classification Trees in Python, From Start To Finish
at Coursera
Josh Starmer
Josh Starmer makes StatQuest videos on statistics and machine learning. Before that he did computational bio-stuff (statistics, mathematics, computing etc) at the University of North Carolina at Chapel Hill
Classification Trees in Python, From Start To Finish at Coursera Entry Requirements
Classification Trees in Python, From Start To Finish
at Coursera
Eligibility criteria
- Prerequisites: In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices.
Conditional Offer
- Not mentioned
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Classification Trees in Python, From Start To Finish at Coursera Students Ratings & Reviews
Classification Trees in Python, From Start To Finish
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Classification Trees in Python, From Start To Finish
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Other: Understood the theory behind Decision Tree. Built DT models with scikit-learn to classify linear and non-linear data Determined the strengths and limitations of Decision Tree
Reviewed on 19 Sep 2021Read More
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Classification Trees in Python, From Start To Finish
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