Predict rocket launch delays with machine learning Microsoft Quiz Answers

Get Predict rocket launch delays with machine learning Microsoft Quiz Answers

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.

Tip:

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

Prerequisites:

Enroll on Microsoft

Module 1: Introduction to rocket launches

Get an introduction to how NASA chooses a date for a rocket launch and discover machine learning fundamentals.

Learning objectives:

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!

Prerequisites:

None

This module is part of these learning paths:

Quiz 1: Knowledge check

Q1. Which statement best describes the role of ethics in data science?

  • Investors should scrutinize the environmental practices of companies that hire data scientists.
  • It’s best to analyze as much data as possible, so you learn what issues you need to fix.
  • Be sure that data inputs are accurate because often, output is used to make policy decisions that affect people’s health and well-being.

Q2. What are the four steps in the data science lifecycle?

  • Business understanding, data gathering and preparation, model training and testing, model deployment
  • Data gathering, data validation, machine learning, result visualization
  • Data identification, model training until 100% accurate, model deployment, business understanding

Q3. What is overfitting in machine learning?

  • When your machine learning model takes up most of the disk space on your server
  • When your machine learning model is so broad, it misidentifies a new item as something it has been trained on.
  • When your machine learning model easily handles new types of items; it’s a good thing.

Q4. What is the goal of manipulating data in data science?

  • To eliminate data that will make it harder to prove what you think must be true.
  • To remove incomplete or inconsequential data, so it doesn’t skew the output away from the truth.
  • To clean up your results, so they are summarized and easy to present.

Q5. What is the role of subject matter expert (SMEs) in the data science lifecycle?

  • SMEs help set the scope of data analysis by identifying factors that will affect the outcome.
  • SMEs add the seal of approval on the results that your data points to.
  • SMEs help you interpret your data in the context of their specialties.

Module 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.

Learning objectives:

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!

Prerequisites:

This module is part of these learning paths:

Quiz 1: Knowledge check

Q1. Why do we need to clean data?

  • So others can read the data more easily
  • Computers get confused by inconsistent data
  • To create better visualizations with data

Q2. Which of the following is not a library we’re using?

  • PyTorch
  • pandas
  • Sklearn
  • NumPy

Module 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.

Learning objectives:

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!

Prerequisites:

This module is part of these learning paths:

Quiz 1: Knowledge check

Q1. Why did we choose a decision tree for our machine learning algorithm?

  • A decision tree is the most complex and accurate algorithm.
  • A decision tree is easy to visualize. It fits well because the model can make only two choices: yes or no.
  • A decision tree has lots of branches, and the model can make lots of choices.

Q2. What is the purpose of splitting your dataset?

  • To make your model more accurate by getting rid of bad data.
  • To try out different algorithms with different data.
  • To have different data for training and testing your model.
Conclusion:

I hope this Predict rocket launch delays with machine learning Microsoft Quiz Answers would be useful for you to learn something new from this problem. If it helped you then don’t forget to bookmark our site for more Coding Solutions.

This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.

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