Applied Machine Learning: Building Models for an Amazon Use Case

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In this lab you will clean data, conduct feature engineering, compare algorithms, and get a firsthand look at how Amazon employees working with machine learning approach ML pipelines.

Level

Fundamental

Duration

2 Hours 0 Minutes

Course Objectives

In this course, you will learn how to:

• Clean data, conduct feature engineering, compare algorithms, and get a firsthand look at how Amazon employees working with machine learning approach ML pipelines
• 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.

Intended Audience

This course is intended for:

• Data Engineers
• Developers

Prerequisites

We recommend that attendees of this course have the following prerequisites:

• 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)

Course Outline

• Task 1: Access Your Amazon SageMaker Notebook Instance
• Task 2: Review and Complete the Provided Notebook