YoVDO

Predict rocket launch delays with machine learning

Offered By: Microsoft via Microsoft Learn

Tags

Machine Learning Courses Python Courses scikit-learn Courses Data Collection Courses Data Manipulation Courses

Course Description

Overview

  • Module 1: Get an introduction to how NASA chooses dates for a rocket launch and discover machine learning fundamentals.

    • 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

    Tip

    This module is part of a multimodal learning experience. Start the module to see how you can follow along!

  • Module 2: 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:

    • 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

    Tip

    This module is part of a multimodal learning experience. Start the module to see how you can follow along!

  • 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.

    • 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.

    Tip

    This module is part of a multimodal learning experience. Start the module to see how you can follow along!


Syllabus

  • Module 1: Introduction to rocket launches
    • Introduction
    • Data to predict weather years in advance
    • Launch day weather analysis
    • Machine learning and the data science lifecycle
    • Set a goal and get expertise
    • Collect, clean, and manipulate data
    • Choose an algorithm and train and test your model
    • Deploy your machine learning model
    • How humans and machine learning models learn
    • Ethics in data science and machine learning
    • Knowledge check
    • Summary
  • Module 2: Data collection and manipulation
    • Introduction
    • Determine the rocket launch questions to ask
    • Explore the rocket launch data to gain an understanding
    • Exercise - Import Python libraries and rocket launch data
    • Exercise - Clean weather data to analyze rocket launch criteria
    • Exercise - Consider additional data to include
    • Knowledge check
    • Summary
  • Module 3: Build a machine learning model
    • Introduction
    • Exercise - Determine columns to include in a machine learning model
    • Exercise - Choose the machine learning algorithm to predict rocket launch success
    • Exercise - Split data into training and testing datasets
    • Exercise - Train and test the machine learning model to predict rocket launch success
    • Exercise - Score the machine learning model that predicts rocket launch success
    • Exercise - Visualize the machine learning model
    • Exercise - Predict the success of a rocket launch using machine learning
    • Knowledge check
    • Summary

Tags

Related Courses

AI For Lawyers (II): Tools for Legal Professionals
National Chiao Tung University via FutureLearn Introducción a la Inteligencia Artificial: Principales Algoritmos
Galileo University via edX Basic Data Analysis and Model Building using Python
Coursera Community Project Network via Coursera Breast Cancer Prediction Using Machine Learning
Coursera Project Network via Coursera Build NLP pipelines using scikit-learn
Coursera Project Network via Coursera