Classification Trees in Python, From Start To Finish
Create Classification Trees in Python
Apply Cost Complexity Pruning in Python
Apply Cross Validation in Python
Create Confusion Matrices in Python
Продемонстрируйте этот практический опыт на собеседовании
In this 1-hour long project-based course, you will 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. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. 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 (e.g. Python, Jupyter, and Tensorflow) pre-installed. 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. Notes: - 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.
Familiarity with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices.
Навыки, которые вы получите
Будете учиться пошагово
На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:
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
Task 8: Cost Complexity Pruning Part 1: Visualize alpha
Task 9: Cost Complexity Pruning Part 2: Cross Validation For Finding the Best Alpha
Task 10: Building, Evaluating, Drawing, and Interpreting the Final Classification Tree
Как устроены проекты с консультациями
Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.
На разделенном экране видео преподаватель предоставляет пошаговые
Рецензии
Лучшие отзывы о курсе CLASSIFICATION TREES IN PYTHON, FROM START TO FINISH
Josh Starmer's videos and courses are always simple and easy to understand. Thank you for this wonderful course. I will definitely recommend everyone to take this course.
A very informative and well guided short session to understand overview of Classification Trees. Covers lot of important concepts in 1 hour. Highly recommend
Awesome Instructor! Like this course. It clears basic knowledge about DecisionTreeClassifier, Tree Pruning, Dealing with missing Data etc.
This is a great course. The instructor does a wonderful job of explaining concepts and providing useful code.
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Часто задаваемые вопросы
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Можно ли скачать свою работу после завершения Проект с консультациями?
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