Search Results for "machine learning projects"

pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,...

Open Source guide, contains resources and materials to learn and get yourself started with Open Source, Git, and GitHub. Building the repo you need to get started with Open Source. This repo contains resources and materials to learn and get yourself started with Open Source, Git and GitHub. The repo content is divided into several Pages for better structuring, easy navigation and making learning easier. Contains 15 days of Twitter threads and blogs that I posted during the Bootcamp. Contains...

...We launched the project back in 2015 with the goal of debunking the "write less do more" myth, meanwhile the Bootstrap developers themselves have decided to drop jQuery, however, we decided to keep maintaining the project for its many benefits and the learning experience. If you mainly use Bootstrap plugins for your projects, why not use a much lighter, potentially faster library, see a different perspective on the same challenges, interact with a less crowded community, and get a better chance to learn or say hi! The Alert component covers the specific original events and public methods, but just like the original plugin, does not provide any initialization option.

unfluff is a Node.js library designed to automatically extract the main content from an HTML document — stripping away navigation bars, ads, footers and other boilerplate to leave you with the “body content”, metadata (title, author, date) and other useful fields. It’s a tool very much aimed at content-analysis, web scraping, building datasets, or repurposing article text for downstream processing (like machine-learning or summarization). The API is simple: you feed in raw HTML and it returns a structured object with the extracted text and other fields. It supports caching internal representations to speed up repeated extractions. While its language support is best for English, it is still widely used in web-content-processing pipelines. ...

This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made.