COURSE

Kaggle Fundamentals

Learn how to get started and participate in Kaggle competitions with our Kaggle Fundamentals course. Kaggle is a data science competition site where you can sign up to compete with other data scientists and data science teams to produce the most accurate analysis of a particular data set. Competition in Kaggle is strong, and placing among the top finishers in a competition will give you bragging rights and an impressive bullet point for your data science resume.

In this course, you will compete in Kaggle's 'Titanic' competition to build a simple machine learning model and make your first Kaggle submission. You will also learn how to select the best algorithm and tune your model for the best performance. You'll be working with multiple algorithms such as logistic regression, k-nearest neighbors, and random forests in attempts to find the model that scores the best and awards you the best rank.

Throughout this course, you'll learn several tips and tricks for competing in Kaggle competitions that will help you place highly. You’ll also learn more about effective machine learning workflows, and about how to use a Jupyter Notebook for Kaggle competitions.

At the end of the course, you’ll have a completed machine learning project and the knowledge you need to dive into other Kaggle competitions and prove your skills to the world.

By the end of this course, you'll be able to:

  • Build a simple machine learning model and make your fist Kaggle submission.
  • Create new features and select the best-performing features to improve your score.
  • Work with multiple algorithms including logistic regression, k nearest neighbors, and random forest.
  • How to select the best algorithm and tune your model for the best performance.

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Learn the Fundamentals for Kaggle

Getting Started with Kaggle

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

Feature Preparation, Selection, and Engineering

Improve your Kaggle score by selecting the best features and creating new ones.

Model Selection and Tuning

Lean how to select the optimum model and tune hyperparameters in Kaggle competitions.

Creating a Kaggle Workflow

Learn how to create and use a machine learning worlflow with Kaggle's Titanic competition.