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Introducing Jupyter Notebooks (AWS SageMaker)

Jupyter Notebooks are the standard tool for interacting with and manipulating data. Data scientists and engineers at many companies can experiment with them, using their datasets to assist in product development. In this activity, we will cover the basic structure of a notebook, how to execute code, and how to make changes. We'll also create a simple machine learning model and use it to make inferences. This lab uses AWS SageMaker Notebooks and provides you with the foundational knowledge required to use this service for more advanced topics. The files used in this lab, can be found [here on GitHub](

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Path Info

Clock icon Beginner
Clock icon 1h 0m
Clock icon Aug 30, 2019

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Table of Contents

  1. Challenge

    Navigate to the Jupyter Notebook

    Navigate through the AWS Console to the AWS SageMaker page. From there, load the Jupyter Notebook server that has been provided with this hands-on lab.

  2. Challenge

    Use Markdown to Add Richly Formatted Text to a Notebook

    Add a cell to the notebook. Make sure the cell is configured for Markdown using the dropdown menu.

    Add some Markdown text. You can try inserting the image that is included in the lab.

  3. Challenge

    Use a Code Cell to Evaluate the Output of Python Code

    Add a cell to the notebook. Make sure the cell is configured for Code using the dropdown menu.

    Add some Python syntax to the cell, and run the cell to see the output.

  4. Challenge

    Use scikit-learn to Build a Simple Machine Learning Model

    All the code you need is provided in the notebook. You can make adjustments to the code, experiment with it, and then run the code in the cells.

    Make a prediction or inference using the generated model.

    Check to see if the prediction matches what you expected using the graph of the model.

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