Applied Machine Learning: Building Models for an Amazon Use Case
SPL-214 - Version 1.0.5
This data set is being provided to you by permission of IMDb and is subject to the terms of the AWS Digital Training Agreement (available at https://aws.amazon.com/training/digital-training-agreement). You are expressly prohibited from copying, modifying, selling, exporting or using this data set in any way other than for the purpose of completing this lab.
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Welcome to the AWS Machine Learning Data Science Capstone: Real World ML Decisions lab where you’ll build, train, and test a machine learning model from the ground up! In this lab you clean data, conduct feature engineering, compare algorithms, and get a firsthand look at how Amazon employees working with machine learning approach ML pipelines.
This lab synthesizes the math-based topics you learned in the Machine Learning Data Scientist path, and you’ll use machine learning to solve a real-life business challenge that the Amazon Studios team faced in the past. This lab is meant to pair with the free digital content for the Machine Learning Data Science Capstone project found here, by selecting your “Learning Library” and searching for “Capstone” https://www.aws.training/learningobject/wbc?id=27201
For the purposes of this lab:
You are assuming the role of a lead data scientist in 2005 and you’re presented with a challenge: Amazon Studios wants to produce award-winning films and, therefore, focus the budget on projects with the best chance of winning those awards. Using the actual dataset from IMDb, an Amazon subsidiary, for movies made between 1990 and 2005, you begin your investigation.
The IMDb dataset is a feature-rich, comprehensive listing of all films released during that time period; it includes critical data such as cast and crew, synopsis, and other production data.
Your task in this lab is to predict which movies will most likely be nominated for an award during the “upcoming” 2005 awards season by building an awards analysis prediction model.
This lab requires:
Access to a notebook computer with Wi-Fi and Microsoft Windows, macOS X, or Linux (Ubuntu, SuSE, or Red Hat)
Note The lab environment is not accessible using an iPad or tablet device, but you can use these devices to access the student guide
For Microsoft Windows users: Administrator access to the computer
An internet browser such as Chrome, Firefox, or Internet Explorer 9 (previous versions of Internet Explorer are not supported)
This lab requires approximately 4 hours to complete.
AWS services not used in this lab
AWS services that are not used in this lab are disabled in the lab environment. In addition, the capabilities of the services used in this lab are limited to what the lab requires. Expect errors when accessing other services or performing actions beyond those provided in this lab guide.
- At the top of your screen, launch your lab by clicking
This will start the process of provisioning your lab resources. An estimated amount of time to provision your lab resources will be displayed. You must wait for your resources to be provisioned before continuing.
If you are prompted for a token, use the one distributed to you (or credits you have purchased).
- Open your lab by clicking
This will automatically log you into the AWS Management Console.
Please do not change the Region unless instructed.
Common login errors
Error : Federated login credentials
If you see this message:
- Close the browser tab to return to your initial lab window
- Wait a few seconds
- Click again
You should now be able to access the AWS Management Console.
Error: You must first log out
If you see the message, You must first log out before logging into a different AWS account:
- Click click here
- Close your browser tab to return to your initial Qwiklabs window
- Click again
Participe do Qwiklabs para ler o restante deste laboratório e muito mais!
- Receber acesso temporário a Console da Amazon Web Services.
- Mais de 200 laboratórios, do nível iniciante ao avançado.
- Tamanho compacto para que você possa aprender no seu próprio ritmo.