Visual Machine Learning with Yellowbrick

4.7
estrelas
65 classificações
oferecido por
Coursera Project Network
3,365 já se inscreveram
Neste projeto guiado, você irá:

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

Diagnose and handle class imbalance problems

Clock2 hours
IntermediateIntermediário
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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.

Habilidades que você desenvolverá

Data ScienceMachine LearningPython ProgrammingData Visualization (DataViz)Scikit-Learn

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

Avaliações

Principais avaliações do VISUAL MACHINE LEARNING WITH YELLOWBRICK

Visualizar todas as avaliações

Perguntas Frequentes – FAQ

Perguntas Frequentes – FAQ

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.