Open-source Python machine learning module

1. Scikit-learn
Scikit-learn is a Python module based on scipy for machine learning and features a variety of classifications, regression and clustering algorithms including support vector machines, logistic regression, naive Bayesian classifier, random forest, Gradient boosting,
Clustering algorithms and Dbscan. and also designed Python numerical and scientific libraries Numpy and Scipy
2.pylearn2
Pylearn is a Theano-based library program that simplifies machine learning research.
3.NuPIC
Nupic is a machine intelligence platform with the HTM learning algorithm as a tool. The HTM is the precise method of computation of the cortex. The core of HTM is the time-based continuous learning algorithm and the storage and revocation space-time patterns. Nupic is suitable for a wide variety of problems, especially detection of anomalies and predictions
Stream data source.
4.Nilearn
Nilearn is a Python module that is able to quickly statistic and learn neural image data. It uses the Scikit-learn Toolkit in the Python language and some applications for predictive modeling, classification, decoding, and connectivity analysis to perform multivariate statistics.
5.PyBrain
Pybrain is based on the Python language reinforcement learning, artificial intelligence, neural network library abbreviation. It aims to compare your algorithms by providing flexible, easy-to-use and powerful machine learning algorithms and testing in a variety of pre-defined environments.
6.Pattern
Pattern is a network mining module under the Python language. It provides tools for data mining, natural language processing, network analysis, and machine learning. It supports vector space model, clustering, support vector machine and perceptron and classifies with KNN classification.
7.Fuel
Fuel provides data for your machine learning model. He has an interface to share data sets such as Mnist, CIFAR-10 (Image dataset), Google's one Billion Words (text). You use him to replace your data in many ways.
8.Bob
Bob is a free tool for signal processing and machine learning. Its toolkit is written in Python and the C + + language, designed to be more efficient and less development time, which is handled by image tools, audio and video processing, machine learning and pattern recognition
A large number of packages are composed.
9.Skdata
Skdata is a library of machine learning and statistical data sets. This module provides standard Python language usage for toy problems, popular computer vision and natural language datasets.
10.MILK
Milk is a machine learning toolkit under the Python language. It mainly uses supervised taxonomy in many available classifications such as SVMS,K-NN, random forest, decision trees. It also performs feature selection. These classifiers are combined in many ways and can form a different example
Unsupervised learning, close relationship with gold dissemination and milk-supported classification systems such as K-means clustering.
11.IEPY
Iepy is an open source information extraction tool that focuses on relational extraction. It is focused on users who need to extract information from large datasets and scientists who want to try out new algorithms.
12.Quepy
Quepy is a Python framework that makes queries in the database query language by altering natural language problems. He can simply be defined as a different type of problem in natural language and database queries. So, you can build your own one with nature without coding.
Language into the system of your database.
Quepy now provides support for SPARQL and MQL query languages. and plan to extend it to other database query languages.
13.Hebel
Hebel is a library program for deep learning of neural networks in the Python language, using Pycuda for GPU and cuda acceleration. It is the most important type of neural network model tool and can provide some different activation functions of the active function,
such as power, Nesterov power, signal loss and stop method.
14.mlxtend
It is a library program that consists of a useful tool and an extension of the daily data Science task.
15.nolearn
This package contains a number of utility modules that can help you complete your machine learning tasks. Many of these modules work with Scikit-learn, and others are usually more useful.
16.Ramp
Ramp is a library program that develops a solution for accelerating prototyping in machine learning in the Python language. He is a lightweight pandas-based machine learning in the pluggable framework, its existing Python language for machine learning and statistical tools
(such as scikit-learn,rpy2, etc.) ramp provides a simple declarative syntax exploration capability to implement algorithms and transformations quickly and efficiently.
17.Feature Forge
This series of tools creates and tests machine learning features through a Scikit-learn-compatible API.
This library program provides a set of tools that will make you very useful in many machine learning programs. When you use the Scikit-learn tool, you will feel a lot of help. (although this can only work if you have different algorithms.) ）
18.REP
Rep is an environment that is provided in a harmonious, renewable way for directing data movement drivers.
It has a uniform classifier wrapper to provide a wide variety of operations, such as TMVA, Sklearn, Xgboost, Uboost and so on. And it can train the classifier in a group in a parallel way. At the same time it also provides an interactive plot.
19.Python Learning Machine Samples
Simple software collection built with Amazon's machine learning.
20.python-elm
This is an implementation of an extreme learning machine based on Scikit-learn in the Python language.

Open-source Python machine learning module