https://github.com/bluelord/kaggle_courses

Kaggle learn course tutorial and exercises notebook
https://github.com/bluelord/kaggle_courses

course ipynb-notebook kaggle-learn python

Last synced: about 14 hours ago
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Kaggle learn course tutorial and exercises notebook

• Host: GitHub
• URL: https://github.com/bluelord/kaggle_courses
• Owner: Bluelord
• License: apache-2.0
• Created: 2021-04-18T19:02:52.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-06-28T17:04:29.000Z (over 3 years ago)
• Last Synced: 2024-11-13T08:42:01.452Z (about 14 hours ago)
• Topics: course, ipynb-notebook, kaggle-learn, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.9 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# README

## Kaggle_Courses

Kaggle is the most popular platform for Data Science. It has multiple free datasets,

projects that you can use for practice, and competitions.

It also has a helpful community where you can share your thoughts and learn new things.

But the best feature of Kaggle is **Kaggle Learn**. Even if you don’t know anything about data science,

you can learn all the basics from Kaggle Courses and then move on to sharpening your skills by doing projects.

In this repository, the Kaggle learn course tutorial and excerses nootbooks(.ipynb) are available

which I have done and earned completion certificates. The dataset used in perticular course are avalable

in that courses folder, the dataset can also be found in kaggel. The courses which are done are as follows:

- ### Courses

  - [**Python**](https://github.com/Bluelord/Kaggle_Courses/blob/02b0089524af5849577d3bd828db05ab20b03674/01%20Python/README.md)

  - [**Pandas**](https://github.com/Bluelord/Kaggle_Courses/blob/cfa3d4df0b90861eb7fdecaca25fe52d93aca4bc/02%20Pandas/README.md)

  - [**Data Visualization**](https://github.com/Bluelord/Kaggle_Courses/blob/f168944b524ad0219075abf5f5374440f7f47ba2/03%20Data%20Visualization/README.md)

  - **Intro to Machine Learning**

  - **Intro to SQL**

  - **Data Cleaning**