After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
1
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.
- Competitions overview50 XP
- Explore train data100 XP
- Explore test data100 XP
- Prepare your first submission50 XP
- Determine a problem type50 XP
- Train a simple model100 XP
- Prepare a submission100 XP
- Public vs Private leaderboard50 XP
- What model is overfitting?50 XP
- Train XGBoost models100 XP
- Explore overfitting XGBoost100 XP
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.
3
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.
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.
Winning a Kaggle Competition in Python
Course
Complete
