2018 will be a year of rapid development of artificial intelligence and machine learning. Some experts said: In contrast, Python is more grounded than Java, and it will naturally become the preferred language for machine learning.
In terms of data science, Python's syntax is the closest to mathematical syntax, making it the easiest language to understand and learn for professionals such as mathematicians or economists. This article will list the top 10 most useful Python tools for machine learning and data science applications
Top 5 Machine Learning Tools
1、Shogun
SHOGUN is a machine learning toolbox focused on learning toolboxes for support vector machines (SVMs). Written in C++ and created as early as 1999, it is one of the oldest machine learning tools, it provides a broad unified machine learning approach, aims to provide transparent and accessible algorithms for machine learning, and provides Free machine learning tools are available to anyone interested in this field.
Shogun provides a well-documented Python interface for unified large-scale learning with high-performance speed. However, the downside of Shogun is that its API is hard to use. (Project address: https://github.com/shogun-toolbox/shogun )
2、Hard
Keras is a high-level neural network API that provides a Python deep learning library. This is the best choice for machine learning for any beginner because it provides an easier way to express neural networks than other libraries. Keras is written in pure Python and is based on Tensorflow, Theano and CNTK backends.
According to the official website, Keras focuses on 4 main guiding principles, namely user-friendliness, modularity, easy extensibility, and collaboration with Python. However, Keras is relatively weak in terms of speed. (Project address: https://github.com/keras-team/keras )
3、scikit-learn
scikit-learn is a Python machine learning project. It is a simple and efficient data mining and data analysis tool. Built on NumPy, SciPy and matplotlib. Scikit-Learn provides a consistent and easy-to-use API grid along with random search. Its main advantage is that the algorithm is simple and fast. The basic functions of Scikit-learn are mainly divided into six parts: classification, regression, clustering, data dimensionality reduction, model selection and data preprocessing (project address: https://github.com/scikit-learn/scikit-learn )
4、Pattern
Pattern is a web mining module that provides tools for data mining, natural language processing, machine learning, web analytics, and web analytics. It also comes with well-documented, 50+ examples and 350+ unit tests passed. Best of all, it's free! (Project address: https://github.com/clips/pattern )
5、Theano
Theano is arguably one of the most mature Python deep learning libraries. Theano is named after the wife of the Greek Pythagorean philosopher and mathematician Pythagoras. Theano's main function: tightly integrated with NumPy, Define the result you want in a symbolic language, and the framework will compile your program to run efficiently on the GPU or CPU.
It also provides tools for defining, optimizing, and evaluating mathematical expressions, and a wealth of other libraries can be built on top of Theano to explore its data structures. Nonetheless, using Theano has some drawbacks; for example, it can take a long time to learn its API, while others feel that the compilation time of Theano large models is not efficient enough (project address: https://github.com /Theano/Theano )
Top 5 Data Science Tools
1、SciPy
SciPy (pronounced "Sigh Pie") is an open source mathematical, scientific and engineering computing package. SciPy provides libraries for commonly used mathematical and scientific programming tasks using various packages such as NumPy, IPython or Pandas. This tool is a great option when you want to manipulate numbers on your computer and display or publish the results, and it's also free. (Project address: https://github.com/scipy/scipy )
2 、 Dask
Dask is a flexible parallel computing library for analytical computing. Also, by changing only a few lines of code, you can quickly parallelize existing code because its DataFrame is the same as in the Pandas library, and its Array objects work like NumPy is able to parallelize written in pure Python. (Project address: https://github.com/dask/dask )
3、Numba
This tool is an open-source optimizing compiler that uses the LLVM compiler infrastructure to compile Python syntax to machine code. The main advantage of using Numba in data science applications is its ability to use NumPy arrays to speed up the application, since Numba is a NumPy-enabled compiler. Just like Scikit-Learn, Numba is also suitable for machine learning applications. (Project address: https://github.com/numba/numba )
4、HPAT
The High Performance Analysis Toolkit (HPAT) is a compiler-based framework for big data. It automatically scales analytics/machine learning code in Python to big data analytics and machine learning in cluster/cloud environments and can optimize specific functions using the @jit decorator. (Project address: https://github.com/IntelLabs/hpat )
5 、 Cython
Cython is your best bet when working with code that runs in mathematical ciphers or cipher loops. Cython is a Pyrex-based source code translator that can quickly generate Python extension modules (extension modules). The Cython language is very close to the Python language, but Cython also supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. (Project address: https://github.com/cython/cython )