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Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

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4 hours16 videos52 exercises
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Course Description

Kaggle is the most famous platform for Data Science competitions. Taking part in such competitions allows you to work with real-world datasets, explore various machine learning problems, compete with other participants and, finally, get invaluable hands-on experience. In this course, you will learn how to approach and structure any Data Science competition. You will be able to select the correct local validation scheme and to avoid overfitting. Moreover, you will master advanced feature engineering together with model ensembling approaches. All these techniques will be practiced on Kaggle competitions datasets.
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In the following Tracks

Machine Learning Scientist in Python

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  1. 1

    Kaggle competitions process

    Free

    In this first chapter, you will get exposure to the Kaggle competition process. You will train a model and prepare a csv file ready for submission. You will learn the difference between Public and Private test splits, and how to prevent overfitting.

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    Competitions overview
    50 xp
    Explore train data
    100 xp
    Explore test data
    100 xp
    Prepare your first submission
    50 xp
    Determine a problem type
    50 xp
    Train a simple model
    100 xp
    Prepare a submission
    100 xp
    Public vs Private leaderboard
    50 xp
    What model is overfitting?
    50 xp
    Train XGBoost models
    100 xp
    Explore overfitting XGBoost
    100 xp
  2. 2

    Dive into the Competition

    Now that you know the basics of Kaggle competitions, you will learn how to study the specific problem at hand. You will practice EDA and get to establish correct local validation strategies. You will also learn about data leakage.

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  3. 4

    Modeling

    Time to bring everything together and build some models! In this last chapter, you will build a base model before tuning some hyperparameters and improving your results with ensembles. You will then get some final tips and tricks to help you compete more efficiently.

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In the following Tracks

Machine Learning Scientist in Python

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datasets

Demand forecasting (train)Demand forecasting (test)House prices (train)House prices (test)Taxi rides (train)Taxi rides (test)

collaborators

Collaborator's avatar
Hillary Green-Lerman
Collaborator's avatar
Hadrien Lacroix
Yauhen Babakhin HeadshotYauhen Babakhin

Kaggle Grandmaster

Yauhen holds a Master’s Degree in Applied Data Analysis and has over 5 years of working experience in Data Science. He worked in Banking, Gaming and eCommerce domains. Yauhen is also the first Kaggle competitions Grandmaster in Belarus having gold medals in both classic Machine Learning and Deep Learning competitions.
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