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 data sets 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](https://github.com/linuxacademy/content-aws-mls-c01)
Successfully complete this lab by achieving the following learning objectives:
- 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.
- 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.
- 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 script to the cell and run the cell to see the output.
- 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 we expect, using the graph of the model.