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Course Outline
Introduction
- Kaggle overview.
- Kaggle categories and performance tiers.
Kaggle Competitions
- Overview of Kaggle competitions.
- Competition formats.
- Joining a Kaggle competition.
- Forming a team.
Kaggle Datasets
- Kaggle dataset types.
- Searching and creating datasets.
- Organizing and collaborating.
Kaggle Kernels
- Kaggle kernel types.
- Searching for kernels.
- Kernel editor and data sources.
- Collaborating on kernels.
Kaggle Public API
- Installing and authenticating.
- Using Kaggle API with competitions.
- Using Kaggle with datasets.
- Creating and maintaining datasets.
- Using Kaggle API with kernels.
- Pushing and pulling a kernel.
- Checking the status and output of a kernel.
- Creating and running a new kernel.
- Kaggle configurations.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Knowledge of machine learning concepts.
- Understanding of statistics.
Audience
- Data scientists.
- Developers.
- Anyone interested in learning Data Science through Kaggle.
14 Hours
