In this course, you will compete in Kaggle’s ‘Titanic’ competition to build a simple machine learning model and make your first Kaggle sublesson. 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.
- Getting Started with Kaggle.
- Feature Preparation, Selection, and Engineering.
- Model Selection and Tuning.
- Guided Project: Creating a Kaggle Workflow.
What will you learn
- How to build a simple machine learning model.
- How to employ multiple algorithms, including logistic regression, k-nearest neighbors, and random forests.
- How to create new features.
- How to select the best algorithm for the best performance.