1. Scikit – learn: Scikit-learn is a Python module based on Scipy for machine learning. It features a variety of classification, regression, and clustering algorithms including support vector machines, logistic regression, Naive Bayes Classifier, Random forest, Gradient Boosting, clustering, and DBSCAN. Python numerical and Scientific Libraries Numpy and Scipy have also been designed
  2. Pylearn2: Pylearn is a Theano-based library that simplifies machine learning.
  3. NuPIC: NuPIC is a machine intelligence platform using HTM learning algorithms as tools. HTM is an accurate method for calculating the cortex. The core of HTM is the time-based continuous learning algorithm and the space-time pattern of storage and revocation. NuPIC is suitable for a wide variety of problems, especially for detecting anomalies and predicting stream data sources.
  4. Nilearn: Nilearn is a Python module that enables rapid statistical learning of neuroimage data. It utilizes Python’s SciKit-Learn toolkit and applications for predictive modeling, classification, decoding, and connectivity analysis to perform multivariate statistics.
  5. PyBrain: PyBrain is short for Python based Reinforcement Learning, Artificial Intelligence, and Neural Network library. Its goal is to provide flexible, easy to use, and powerful machine learning algorithms and compare your algorithms with tests in a variety of predefined environments.
  6. Pattern: Pattern is a Python network mining module. It provides tools for data mining, natural language processing, network analysis and machine learning. It supports vector space models, clustering, support vector machines and perceptrons and is classified by KNN classification.
  7. Fuel: Fuel provides data for your machine learning model. He has an interface for sharing datasets such as MNIST, CIFAR-10 (image dataset), and Google’s One Billion Words (text). You use it to substitute your own data in a variety of ways.
  8. Bob: Bob is a free signal processing and machine learning tool. Its toolkit, written in Python and C++, is designed to be more efficient and reduce development time, and is made up of numerous software packages for processing image tools, audio and video processing, machine learning, and pattern recognition.
  9. Skdata: Skdata is a library for machine learning and statistical data sets. This module provides use of the standard Python language for toy problems, popular computer vision and natural language datasets.
  10. MILK: MILK is a machine learning toolkit in Python. It mainly uses supervised taxonomy in many available classifications such as SVMS,K-NN, random forest, and decision tree. It also performs feature selection. The combination of these classifiers in many aspects can form different classification systems such as unsupervised learning, close relationship gold propagation and K-means clustering supported by MILK.
  11. IEPY: IEPY is an open source information extraction tool focused on relationship extraction. It is aimed at users who need to extract information from large data sets and scientists who want to try out new algorithms.
  12. Quepy: Quepy is a Python framework for changing natural language problems to make queries in the database query language. It can simply be defined as different types of problems in natural language and database queries. So, you can build your own system for accessing your database in natural language without coding. Quepy now provides support for Sparql and MQL query languages. There are plans to extend it to other database query languages.
  13. Hebel: Hebel is a Python library for deep learning of neural networks. It uses PyCUDA for GPU and CUDA acceleration. It is the most important type of neural network model tool and can provide activation functions for several different activity functions, such as dynamic, Nesterov dynamic, signal loss and stop method.
  14. Mlxtend: It is a library of useful tools and extensions for everyday data science tasks.
  15. Nolearn: This package contains a number of utility modules that can help you accomplish machine learning tasks. Many of these modules work with SciKit-Learn, while others are often more useful.
  16. Ramp: Ramp is a Python library for developing solutions to speed up prototyping in machine learning. Ramp is a lightweight pluggable framework for pandas based machine learning. Its existing Python machine learning and statistics tools (sciKit-learn, RPY2, etc.), Ramp, provide a simple declarative syntax exploration to implement algorithms and transformations quickly and efficiently.
  17. Feature Forge: This suite of tools creates and tests machine learning functionality through sciKit-learn compatible apis. This library provides a set of tools that you’ll find useful in many machine learning programs. When you use the SciKit-learn tool, you will feel greatly helped. (Although this only works if you use a different algorithm.)
  18. REP: REP is an environment for directing data movement drives in a harmonious and renewable manner. It has a unified classifier wrapper to provide various operations, such as TMVA, Sklearn, XGBoost, uBoost, and so on. And it can train classifiers in parallel in a population. It also provides an interactive plot.
  19. Python Learning Machine Samples: A collection of simple software built with Amazon’s machine learning.
  20. Python-elm: This is an implementation of an extreme learning machine based on Scikit-learn in Python.