Data-science Libraries

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Data-science Libraries

MLBox is a powerful Automated Machine Learning python library.

MLBox has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

Collection of cheatsheets for data science, machine learning and deep learning :).

Here, you can find all cheatsheets for Data Science, Machine Learning and Deep Learning.

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

xLearn is developed by high-performance C++ code with careful design and optimizations. Our system is designed to maximize CPU and memory utilization, provide cache-aware computation, and support loc…

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

You can start with this article that I wrote in Heartbeat magazine (on Medium platform): Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, …

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Install CatBoost by following the guide for the Next you may want to investigate: If you cannot open documentation in your browser try adding yastatic.net and yastat.net to the list of allowed domain…

Tutorial: Web scraping in Python with Beautiful Soup

Just want to see the code? Here it is:

Algorithmic trading using machine learning.

This machine learning algorithm was built using Python 3 and scikit-learn with a Decision Tree Classifier. The program gathers stock data using the Google Finance API and pandas. The data is illustra…

A repository for some of the code I used in kaggle data science & machine learning tasks.

This repository contains some of the code I use in Kaggle data science/machine learning tasks. Where appliciable I keep all the code/data from a single competition within one a folder. i.e. 'californ…

Machine Learning and Data Analysis Coursera Specialization from Yandex and MIPT

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana. The recommended installation method is to use pip within a Python 3.x virtalenv. You can use dsio thro…

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use.…

An open source python library for automated feature engineering

Install with pip You can install add-ons individually or all at once by running: Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, w…

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

We provide here a suite of Python examples that walk you through concepts in: Classical & Deep Reinforcement Learning Basic & Advanced Machine Learning Anson Wong

A blog for data analytics using data science technologies

MIT License Anyone may contribute to our project. Submit a pull request or raise an issue.

Achoo uses a Raspberry Pi to predict if my son will need his inhaler on any given day using weather, pollen, and air quality data. If the prediction for a given day is above a specified threshold, the Pi will email his school nurse, and myself, notifying her that he may need preemptive treatment. Community-sourced health monitoring!

Blue button is for inhaler (first line of defense) red button is for breathing treatment (second line of defense; use this one if his symptoms are really bad)

H2O.ai Machine Learning Interpretability Resources

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques i…

Machine Learning notebooks for refreshing concepts.

Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet. Linear & Multiple Regression Logistic Regression Regularization

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

Jupyter Notebooks on the topics of statistical analysis, mathematics, and numerical/sceintific computing (in Python).

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you sta…

IPython widgets, interactive plots, interactive machine learning

Notebooks come alive when interactive widgets are used. Users can visualize and control changes in the data and the model. Learning becomes an immersive, plus fun, experience. However, learning and e…

A collection of demos showcasing automated feature engineering and machine learning in diverse use cases

Building an accurate machine learning model requires several important steps. One of the most complex and time consuming is extracting information through features. Finding the right features is a cr…

The code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.

My interests lie in the intersection of statistical techniques, data visualization and sports (especially football). All the codes are written entirely in Python or R. I don't have a strong preferenc…

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

This notebook contains the code required to conduct a Bayesian data analysis on data collected from a set of multiple-lot online auction events executed in Europen markets, over the course of a year.…

Multi-class confusion matrix library in Python

Fig1. ConfusionMatrix Block Diagram After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of seven overall benchmar…

DATA-SCIENCE-BOWL-2018 Find the nuclei in divergent images to advance medical discovery

Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The 2018 Data Science Bowl offers our most ambitious mission yet: create an algorithm to…

Repository for a data science starter app using Flask, Angular and Docker. https://medium.com/@dvelsner/deploying-a-simple-machine-learning-model-in-a-modern-web-application-flask-angular-docker-a657db075280

First fork or clone this repo: After cloning the repository go inside the project folder: I highly recommend the use of docker and docker-compose as it is far simpler to get started than to run all o…

A CNN which converts piano audio to a simplified MIDI format

A Convolutional Neural Network which converts piano audio to a simplified MIDI format.The final model takes as input an audio file (mono- or polyphonic)and outputs a simplified MIDI output with the c…