How to Load Kaggle Datasets Directly into Google Colab?

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Almost every data science aspirant uses Kaggle. It houses datasets for every domain. You can get a dataset for every possible use case ranging from the entertainment industry, medical, e-commerce, and even astronomy. Its users practice on various datasets to test out their skills in the field of Data Science and Machine learning.

The Kaggle datasets can have varying sizes. Some datasets can be as small as under 1MB and as large as 100 GB. Also, some of the Deep learning practices require GPU support that can boost the training time. Google Colab is a promising platform that can help beginners to test out their code in the cloud environment.

Step 1: Select any dataset from Kaggle

Step 2: Download API Credentials

Step 3: Setup the Colab Notebook

Step 4: Download datasets

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https://www.analyticsvidhya.com/blog/2021/06/how-to-load-kaggle-datasets-directly-into-google-colab/

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Google's Python Class

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Welcome to Google's Python Class -- this is a free class for people with a little bit of programming experience who want to learn Python. The class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience. The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and http connections. The class is geared for people who have a little bit of programming experience in some language, enough to know what a "variable" or "if statement" is. Beyond that, you do not need to be an expert programmer to use this material.

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https://developers.google.com/edu/python/

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Getting Started With Google Colab Python

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The ‘Colaboratory’ Data Scientist:

The case for using Google Colab for data science in the cloud.

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https://towardsdatascience.com/the-colaboratory-data-scientist-working-in-the-cloud-2a48fc967e00

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Colab Python Machine Learning With Scikit-API

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To introduce the fundamental vocabulary and concepts of machine learning.

To introduce the Scikit-Learn API and show some examples of its use.

To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.

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https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb

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Colab Python Statistics

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In this section, we will introduce three types of statistics inference methods: evaluating and comparing estimators, conducting hypothesis tests, and constructing confidence intervals. These methods can help us infer the characteristics of a given population, i.e., the true parameter θ. For brevity, we assume that the true parameter θ of a given population is a scalar value. It is straightforward to extend to the case where θ is a vector or a tensor, thus we omit it in our discussion.

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https://colab.research.google.com/github/d2l-ai/d2l-en-colab/blob/master/chapter_appendix-mathematics-for-deep-learning/statistics.ipynb

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