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

  • Python programming skills
  • Knowledge of machine learning
  • Understanding of statistics

Audience

  • Data scientists
  • Developers
  • Anyone who wants to learn Data Science using Kaggle
  14 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

Testimonials (5)

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