Open source is at the heart of technological innovation and rapid development. This article shows you the Python machine learning open source project and the very interesting insights and trends found during the analysis. We analyzed the top 20 Python machine learning projects on GitHub and found that scikit-Learn, PyLearn2 and NuPic are the most active projects. Let's explore these popular projects on Github! 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 machine, logistic regression, naive Bayes classifier, Random forest, Gradient BoosTIng, clustering algorithm and DBSCAN. Also designed Python numerical and scienTIfic libraries Numpy and Scipy Pylearn2: Pylearn is a library program based on Theano that simplifies machine learning research. NuPIC: NuPIC is a machine intelligence platform based on HTM learning algorithms. HTM is an accurate calculation method for the cortex. The core of HTM is a time-based continuous learning algorithm and a spatiotemporal pattern of storage and revocation. NuPIC is suitable for a wide variety of problems, especially for detecting anomalies and predicting sources of streaming data. Nilearn: Nilearn is a Python module that quickly counts and learns neuroimaging data. It uses the scikit-learn toolbox in the Python language and some applications for predictive modeling, classification, decoding, and connectivity analysis to perform multivariate statistics. PyBrain: Pybrain is an acronym for reinforced learning, artificial intelligence, and neural network libraries based on the Python language. Its goal is to provide flexible, easy-to-use and powerful machine learning algorithms and perform a variety of pre-defined environment tests to compare your algorithms. Pattern:Pattern is a network mining module in Python language. 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 classifies them using the KNN classification. Fuel:Fuel provides data for your machine learning model. He has an interface that shares data sets such as MNIST, CIFAR-10 (picture dataset), and Google's One Billion Words (text). You use him to replace your data in a variety of ways. Bob: Bob is a free tool for signal processing and machine learning. Its toolkit is written in Python and C++. It is designed to be more efficient and reduce development time. It is composed of a large number of software packages for processing image tools, audio and video processing, machine learning and pattern recognition. of. Skdata: Skdata is a library program for machine learning and statistical data sets. This module provides a standard Python language for toy issues, popular computer vision and natural language datasets. MILK: MILK is a machine learning toolkit for Python. It mainly uses supervised taxonomy in many available categories such as SVMS, K-NN, random forest, and decision trees. It also performs feature selection. These classifiers combine in many ways to form different classification systems such as unsupervised learning, affinity gold propagation, and K-means clustering supported by MILK. IEPY: IEPY is an open source information extraction tool that focuses on relational extraction. It is aimed at users who need to extract information from large data sets and scientists who want to try new algorithms. Quepy: Quepy is a Python framework for querying in the database query language by changing natural language problems. He can be simply defined as different types of problems in natural language and database queries. So, you can build your own system that uses natural language to get into your database without coding. Quipy now provides support for Sparql and MQL query languages. And plan to extend it to other database query languages. Hebel: Hebel is a library program for deep learning of neural networks in the Python language. 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 a number of different activity functions such as power, Nesterov power, signal loss and stopping. Mlxtend: It is a library program consisting of useful tools and extensions to everyday data science tasks. Nolearn: This package contains a number of utility modules that can help you with your machine learning tasks. A large number of modules work with scikit-learn, others are usually more useful. Ramp: Ramp is a library program that develops solutions for speeding up prototyping in machine learning in the Python language. He is a lightweight pandas-based machine learning pluggable framework, its existing Python language machine learning and statistical tools (such as scikit-learn, rpy2, etc.) Ramp provides a simple declarative syntax exploration function to enable Implement algorithms and transformations quickly and efficiently. Feature Forge: This series of tools creates and tests machine learning capabilities through an API that is compatible with scikit-learn. This library program provides a set of tools that will make you useful in many machine learning programs. When you use the scikit-learn tool, you will feel a lot of help. (Although this only works when you use a different algorithm.) REP: REP is an environment in which a data movement driver is commanded in a harmonious and reproducible manner. It has a unified classifier package to provide a variety of operations, such as TMVA, Sklearn, XGBoost, uBoost and more. And it can train the classifier in a parallel in a group. It also provides an interactive plot. Python Learning Machine Sample: A simple software collection built with Amazon's machine learning. Python-ELM: This is an implementation of an extreme learning machine based on scikit-learn in Python. Bulk usb drives for sale,Bulk USB Drives - Premium USB,custom USB drives,Amazon flash drive,USB memory direct,Promotional USB drives Shenzhen Konchang Electronic Technology Co.,Ltd , https://www.konchang.com