Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 1: Literacy and Foundations
Predict Rocket Launch Delays with Machine Learning
This learning path introduces you to the world of machine learning. You'll take a real-life problem that NASA faces and apply machine learning to solve it. The goal is to get students excited and curious to discover how machine learning could help solve other problems in space discovery and different aspects of life.
Courses in this program
1) Introduction to Rocket Launches
Get an introduction to how NASA chooses a date for a rocket launch and discover machine learning fundamentals.
In this module, you'll begin to discover:
In this module, you'll begin to discover:
- The challenges weather can pose for a rocket launch.
- The data science lifecycle.
- How machine learning works.
- The role ethics play in machine learning.
Estimated effort 1 hour
English language
2) Data Collection and Manipulation
Learn about the steps to import data into Python and clean the data for use in creating machine learning models.
In this module, you will:
In this module, you will:
- 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.
3) Build a Machine Learning Model
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.
In this module, you'll begin to discover:
In this module, you'll begin to discover:
- 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.