Predictive Modelling with Azure Machine Learning Studio

204 calificaciones
ofrecido por
Coursera Project Network
8,571 ya inscrito
En este proyecto guiado, tú:

Build a predictive model using Azure ML Studio

Demonstrate a working knowledge of setting up experiments on Azure ML Studio

Operationalise machine learning workflows with Azure's drag-and-drop modules

Clock2 hours
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

In this project, we will use Azure Machine Learning Studio to build a predictive model without writing a single line of code! Specifically, we will predict flight delays using weather data provided by the US Bureau of Transportation Statistics and the National Oceanic and Atmospheric Association (NOAA). You will be provided with instructions on how to set up your Azure Machine Learning account with $200 worth of free credit to get started with running your experiments! 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, and scikit-learn pre-installed. 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.

Habilidades que desarrollarás

Data ScienceArtificial Intelligence (AI)Machine LearningData AnalysisMicrosoft Azure

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction and Setup Instructions

  2. Importing the Data Sets

  3. Scrubbing Missing Values

  4. Eliminating Target Leaks

  5. Conversion to Categorical Features

  6. Preparing Features to be Joined with Weather Data

  7. Preprocessing the Weather Dataset

  8. Joining Both Datasets

  9. Training and Evaluating the Model

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso




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