This is an introductory tutorial aimed at users with little or no programming experience. This tutorial should be enough for anyone to explore and run code on Kaggle Notebooks (Ks).
A:
Take a look at Kaggle Notebooks. It is actually an online repository of high quality machine learning (ML) models that have been developed over time by the Kaggle community (and Kaggle members). It is a bit like Heroku for Machine Learning (ML).
By using Notebooks, you don't need to know a lot of programming, so it is pretty easy. The code can be run from the Notebook and it will generate pretty results (e.g. predictions).
Here are the main Notebooks available on Kaggle Notebooks:
SciKit Learn models (like Random Forest and Naïve Bayes): They are easy to understand and implement.
Kaggle Kernels (for many kernels): Many of the kaggle kernels have the "notebook" version where you can find the code and the nice visualizations.
Kaggle Notebooks: you don't need to know programming to use them.
If you're interested in running machine learning on personal datasets and/or experimenting with different approaches, I'd highly recommend checking out the Kaggle Community Notebook site. The Notebook is a collection of notebooks that model various machine learning algorithms. Each notebook contains sample data, code for each step of the model, and output of the model in order to reproduce results.
Here's a tutorial on how to use the Notebook
and here's a list of some of the great resources in the site.
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