Overview
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- Module 1: Get an introduction to how NASA chooses dates for a rocket launch and discover machine learning fundamentals.
- The challenges weather can pose for a rocket launch
- The data science lifecycle
- How machine learning works
- The role ethics play in machine learning
- Module 2: Learn about the steps to import data into Python and clean the data for use in creating machine learning models.
- Explore weather data on days crewed and uncrewed rockets were launched
- Explore weather data on the days surrounding launch days
- Clean the data in preparation for training the machine learning model
- Module 3: In this module you focus on a local analysis of your data by using scikit-learn, and use a decision tree classifier to gain knowledge from raw weather and rocket launch data.
- The importance of column choosing.
- How to split data to effectively train and test a machine learning algorithm.
- How to train, test, and score a machine learning algorithm.
- How to visualize a tree classification model.
In this module, you'll begin to discover:
Tip
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
In this module, you will:
Tip
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
In this module, you'll begin to discover:
Tip
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
