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Course Outline
Introduction
- Overview of Kaggle
- Kaggle categories and performance tiers
Kaggle Competitions
- Overview of Kaggle competitions
- Competition formats
- Joining a Kaggle competition
- Forming a team
Kaggle Datasets
- Kaggle types of datasets
- 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
