Scikit-learn |
The famous Scikit-learn,scikit-learn is an open-source machine learning toolkit based on NumPy, SciPy, matplotlib, mainly covering classification, regression and clustering algorithms such as SVM, logistic regression, Naive Bayes, random forest, Algorithms such as K-means, code and documentation are very good, and are used in many Python projects. For example, in our familiar NLTK, the classifier has an interface specifically for Scikit-learn, which can call Scikit-learn's classification algorithm and train data to train the classifier model. A video is recommended here, and I recommended it when I met Scikit-learn in the early days: A python machine learning Toolkit Scikit-learn and related video –tutorial:scikit-learn–machine are recommended learning In Python official homepage: http://scikit-learn.org/ |
Pandas |
Pandas is also based on NumPy and matplotlib development, mainly for data analysis and data visualization, its data structure Dataframe and R language Data.frame very much like, especially for time series data has its own set of analysis mechanism, very good. Here is a book, "Python for Data analysis", the author is the main development of pandas, introduced in turn Ipython, NumPy, pandas related functions, data visualization, data cleaning and processing, time data processing, etc. Examples include financial stock data mining and so on, quite good. Official homepage: http://pandas.pydata.org/ |
Mlpy |
Official homepage: http://mlpy.sourceforge.net/ |
Mdp |
MDP's modular Toolkit for data processing, a Python data processing framework. From the user's point of view, MDP is a group of supervised learning and unsupervised learning algorithms and other data processing units that can be integrated into data processing sequences and more complex feedforward network structures. Calculations are performed efficiently according to speed and memory requirements. From a scientific developer's point of view, MDP is a modular framework that can be easily extended. The implementation of the new algorithm is easy and intuitive. The newly implemented unit is then automatically integrated with the rest of the library's components. MDP was written in the context of neuroscience research, but it has been designed to be useful in any situation where training data processing algorithms can be used. Its simplicity on the user side, various readily available algorithms, and reusability of the application unit make it a useful teaching tool. "Official homepage: http://mdp-toolkit.sourceforge.net/ |
Pybrain |
Pybrain (python-based reinforcement Learning, Artificial Intelligence and Neural Network) is a machine learning module for Python, Its goal is to provide a flexible, easy-to-apply, powerful machine learning algorithm for machine learning tasks. (this name is very domineering) Pybrain as its name includes neural networks, reinforcement learning (and the combination of both), unsupervised learning, evolutionary algorithms. Because many of the current problems require processing of continuous state and behavior space, function approximations (such as neural networks) must be used to cope with high-dimensional data. Pybrain the neural network as the core, all the training methods are based on the neural network as an example. "Official homepage: http://www.pybrain.org/ |
Pyml |
"Pyml is a Python machine learning toolkit that provides a flexible architecture for each classification and regression approach. It mainly provides feature selection, model selection, combinatorial classifier, classification evaluation and other functions. ” |
Milk |
Machine Learning Toolkit in Python. "Milk is a machine learning toolkit for Python that focuses on providing supervised taxonomies with several effective classification analyses: SVMs (based on LIBSVM), K-nn, stochastic forest economics and decision trees. It also allows for feature selection. These classifications can be combined in many ways to form different classification systems. For unsupervised learning, it provides k-means and affinity propagation clustering algorithms. ” Official homepage: Http://luispedro.org/software/milkhttp://luispedro.org/software/milk |
Pymvpa |
Multivariate Pattern Analysis (MVPA) in Python The PYMVPA (multivariate Pattern analysis in Python) is a Python toolkit that provides statistical learning analytics for large datasets, providing a flexible and extensible framework. It provides functions such as classification, regression, feature selection, data import and export, visualization, etc. official homepage: http://www.pymvpa.org/ |
Pyrallel |
Parallel Data Analytics in Python "Pyrallel (Parallel Data Analytics in Python) based on the distributed computing model of machine learning and semi-interactive pilot project, can run on a small cluster" GitHub code page: Http://github.com/pydata/pyrallel |
Monte |
Gradient based Learning in Python "Learning in pure Python is a pure Python machine learning Library. It can quickly build neural networks, conditional random-airports, logistic regression models, use INLINE-C optimization, easy to use and expand. "Official homepage: http://montepython.sourceforge.net |
Theano |
The Theano is a Python library that defines, optimizes, and simulates mathematical expression calculations for efficient resolution of multidimensional array calculations. Theano Features: Tightly integrated numpy, efficient data-intensive GPU computing, efficient symbolic differential operations, high-speed and stable optimization, dynamic generation of C code, extensive unit testing and self-validation. Since 2007, Theano has been widely used in scientific operations. Theano makes it easier to build deep learning models and can quickly implement multiple models. Ps:theano, a Greek beauty, the daughter of Croton's most powerful Milo, later became Pythagoras ' wife. |
Pylearn2 |
"Pylearn2 built on Theano, part of the reliance on Scikit-learn, the current Pylearn2 is in development, will be able to deal with vectors, images, video and other data, to provide MLP, RBM, SDA and other deep learning model. "Official homepage: http://deeplearning.net/software/pylearn2/ |