Getting Started With Kaggle

Learn how to make your first submission to the Titanic competition.


  • Learn how to approach a Kaggle competition and explore the competition data.
  • Learn techniques for cleaning and preparing data for machine learning.
  • Learn how to train a machine learning model and make your first Kaggle submission.

Mission Outline

1. Introduction to Kaggle
2. Exploring the Data
3. Exploring and Converting the Age Column
4. Preparing our Data for Machine Learning
5. Creating Our First Machine Learning Model
6. Splitting Our Training Data
7. Making Predictions and Measuring their Accuracy
8. Using Cross Validation for More Accurate Error Measurement
9. Making Predictions on Unseen Data
10. Creating a Submission File
11. Making Our First Submission to Kaggle
12. Next Steps
13. Takeaway


Course Info:


The median completion time for this course is 5.9 hours. View Details

This course requires a premium subscription, and includes three missions and one guided project.  It is the 28th course in the Data Scientist in Python path.


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