Open Source Machine Learning Software

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, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic). File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others).

API and command line utility, written in Python, for querying Elasticsearch exporting result as documents into a CSV file. The search can be done using logical operators or ranges, in combination or alone. The output can be limited to the desired attributes. Also ToT can insert the querying to a Pandas Dataframe or/and save its in a HDF5 container (under development).

This project aimed at creating framework and binary data format for etymological Arabic system. and will not continue hosted at sourceforge because the term of use determine me as enemy, so I am prohibited from using sourceforge services.

Blunder is an automated tool for analyzing chained exceptions in Java. It's usefull for classify, generate a customized error message and a list for possible solutions.

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. The dataset is not one of the standard sets like MNIST or CIFAR, you will make you very own dataset. Then you will go through a couple conventional machine learning algorithms, before finally getting to deep learning! In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class.

The objective of this project is to make available an open-source of a gridified version of the Multifactor Dimensionality Reduction (MDR) software (http://www.epistasis.org/software.html).

The aim of ALIVE is to develop new approaches to the engineering of flexible, adaptable distributed service-oriented systems based on the adaptation of social coordination and organisation mechanisms.

LPCforSOS is a machine learning framework with a special focus on structured output spaces and pairwise learning. It supports currently multiclass, ordinal, hierarchical, multi-label and label ranking classification settings.

SAIM allows to interlink knowledge bases in the Semantic Web. It focuses on instance matching of very large knowledge bases available as SPARQL endpoints. SAIM uses machine learning techniques and is compatible with SILK.