Its focus is on supervised classification with several classifiers available:
SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs
feature selection. These classifiers can be combined in many ways to form
different classification systems.
“Milk是Python的一个机器学习工具箱，其重点是提供监督分类法与几种有效的分类分析：SVMs(基于libsvm)，K-NN，随机森林经济和决策树。它还可以进行特征选择。这些分类可以在许多方面相结合，形成不同的分类系统。对于无监督学习，它提供K-means和affinity propagation聚类算法。”
官方主页：
8. PyMVPA: MultiVariate Pattern Analysis (MVPA) in Python
PyMVPA is a Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, and MDP. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is free software and requires nothing but free-software to run.
“PyMVPA(Multivariate Pattern Analysis in Python)是为大数据集提供统计学习分析的Python工具包，它提供了一个灵活可扩展的框架。它提供的功能有分类、回归、特征选择、数据导入导出、可视化等”
官方主页：
9. Pyrallel – Parallel Data Analytics in Python
Experimental project to investigate distributed computation patterns for machine learning and other semi-interactive data analytics tasks.
“Pyrallel(Parallel Data Analytics in Python)基于分布式计算模式的机器学习和半交互式的试验项目，可在小型集群上运行”
Github代码页：
10. Monte – gradient based learning in Python
Monte (python) is a Python framework for building gradient based learning machines, like neural networks, conditional random fields, logistic regression, etc. Monte contains modules (that hold parameters, a cost-function and a gradient-function) and trainers (that can adapt a module’s parameters by minimizing its cost-function on training data).
Modules are usually composed of other modules, which can in turn contain other modules, etc. Gradients of decomposable systems like these can be computed with back-propagation.
“Monte (machine learning in pure Python)是一个纯Python机器学习库。它可以迅速构建神经网络、条件随机场、逻辑回归等模型，使用inline-C优化，极易使用和扩展。”
官方主页：
11. Theano
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:
1）tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
2）transparent use of a GPU – Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
3）efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
4）speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
5）dynamic C code generation – Evaluate expressions faster.
6） extensive unit-testing and self-verification – Detect and diagnose many types of mistake.
Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).
“Theano 是一个 Python 库，用来定义、优化和模拟数学表达式计算，用于高效的解决多维数组的计算问题。Theano的特点：紧密集成Numpy；高效的数据密集型GPU计算；高效的符号微分运算；高速和稳定的优化；动态生成c代码；广泛的单元测试和自我验证。自2007年以来，Theano已被广泛应用于科学运算。theano使得构建深度学习模型更加容易，可以快速实现多种模型。PS：Theano，一位希腊美女，Croton最有权势的Milo的女儿，后来成为了毕达哥拉斯的老婆。”
12. Pylearn2
Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).
“Pylearn2建立在theano上，部分依赖scikit-learn上，目前Pylearn2正处于开发中，将可以处理向量、图像、视频等数据，提供MLP、RBM、SDA等深度学习模型。”
官方主页：
其他的，欢迎大家补充，这里也会持续更新这篇文章。