Interpretable Machine Learning Applications: Part 1

oferecido por
Coursera Project Network
Neste projeto guiado, você irá:

How to select and compare different prediction models (classification regressors) for a real world dataset (FIFA 2018 Soccer World Cup Statistics).

How to extract the most important features, which impact the classifiers, in a model-agnostic approach, together with caveats.

How to get an insight into the way values of the most important features impact the predictions made by the classifiers.

Clock2-hour course, including time of video recordings, practicing and readings, taking the quiz.
BeginnerBásico
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications. Note: 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

Python basic knowledgeFeatures engineeringMachine learning classification (regression) models

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. Setting the stage (Python Jupyter Lab web-based Server environment, importing the dataset and file to train and test the designated classification regressors as prediction models).

  2. Train, test and estimate the accuracy (confusion matrix) of a Decision Tree classifier.

  3. Train, test and estimate the accuracy (confusion matrix) of a Random Tree classifier as an alternative to the previous one.

  4. Extract a ranking list of the features, which are most important for each one of our prediction models.

  5. Extract and plot the impact of the values of selected important features on predictions being made by each one of our prediction models.

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

Perguntas Frequentes – FAQ

Perguntas Frequentes – FAQ

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