Visual Machine Learning with Yellowbrick

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Coursera Project Network
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在此指導項目中,您將:

Evaluate the performance of a classifier using visual diagnostic tools from Yellowbrick

Diagnose and handle class imbalance problems

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

Welcome to this project-based course on Visual Machine Learning with Yellowbrick. In this course, we will explore how to evaluate the performance of a random forest classifier on the Poker Hand data set using visual diagnostic tools from Yellowbrick. With an emphasis on visual steering of our analysis, we will cover the following topics in our machine learning workflow: feature analysis, feature importance, algorithm selection, model evaluation using regression, cross-validation, and hyperparameter tuning. 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, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

您要培養的技能

  • Data Science
  • Machine Learning
  • Python Programming
  • Data Visualization (DataViz)
  • Scikit-Learn

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction to the Project and Dataset

  2. Separate the Data into Features and Targets

  3. Evaluating Class Balance

  4. Up-sampling from Minority Classes

  5. Training a Random Forests Classifier

  6. Classification Accuracy

  7. ROC Curve and AUC

  8. Classification Report Heatmap

  9. Class Prediction Error

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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