Course overview

In this course, you’ll learn how to participate in Kaggle competitions. Kaggle is a data science competition site where you can compete with other data scientists and data science teams to produce the most accurate analysis of a particular dataset. Competition in Kaggle is fierce, and placing among the top finishers in a competition is an impressive accomplishment to include in 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 learn how to select the best algorithm and tune your model for the best performance — and how to work with multiple algorithms, such as logistic regression, k-nearest neighbors, and the random forest algorithm to find the model that scores the best and awards you the highest rank.

Throughout this course, you’ll not only learn several tips and tricks for competing in Kaggle competitions but also how to use a Jupyter Notebook for Kaggle competitions.

At the end of the course, you’ll complete a machine learning project.

Key skills

    • Building a simple machine learning model and making your fist Kaggle submission
    • Creating new features and selecting the best-performing features to improve your score
    • Working with multiple algorithms, including logistic regression, k-nearest neighbors, and random forest
    • Selecting the best algorithm and tuning your model for the best performance

Course outline

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Projects in this course

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