Winning a Kaggle Competition in Python
Offered By: DataCamp
Course Description
Overview
Learn how to approach and win competitions on Kaggle.
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.
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.
Syllabus
- Kaggle competitions process
- 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.
- 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.
- Feature Engineering
- You will now get exposure to different types of features. You will modify existing features and create new ones. Also, you will treat the missing data accordingly.
- 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.
Taught by
Yauhen Babakhin
Related Courses
Practical Machine LearningJohns Hopkins University via Coursera Practical Deep Learning For Coders
fast.ai via Independent 機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
National Taiwan University via Coursera Data Analytics Foundations for Accountancy II
University of Illinois at Urbana-Champaign via Coursera Entraînez un modèle prédictif linéaire
CentraleSupélec via OpenClassrooms