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Course Outline
Introduction
- Kaggle overview
- Kaggle categories and performance tiers
Kaggle Competitions
- Overview of Kaggle competitions
- Competition formats
- Participating in a Kaggle competition
- Assembling a team
Kaggle Datasets
- Types of datasets available on Kaggle
- Searching for and creating datasets
- Organizing and collaborating on data
Kaggle Kernels
- Types of Kaggle kernels
- Locating kernels
- Kernels editor and data sources
- Collaborative work on kernels
Kaggle Public API
- Installation and authentication processes
- Utilizing the Kaggle API for competitions
- Utilizing the Kaggle API for datasets
- Creating and managing datasets
- Utilizing the Kaggle API for kernels
- Pushing and pulling kernels
- Monitoring kernel status and outputs
- Creating and executing new kernels
- Kaggle configuration settings
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Familiarity with machine learning principles
- Understanding of statistical concepts
Target Audience
- Data scientists
- Software developers
- Individuals interested in learning Data Science via Kaggle
14 Hours
