10 most popular machine learning and data Science python libraries

2018 will be a year of rapid growth in AI and machine learning, experts say: Compared to Python is more grounded than Java, and naturally becomes the preferred language for machine learning

In data science, Python's grammar is the closest to mathematical grammar, making it the easiest language for professionals such as mathematicians or economists to understand and learn. This article will list the top ten most useful Python tools for machine learning and data science applications

Machine learning Tools

1, Shogun

Shogun is a machine learning toolkit that focuses on support vector machine (SVM) Learning Toolkit. Written in C + +, which was created as early as 1999, it is one of the oldest machine learning tools, providing a wide range of unified machine learning methods designed to provide transparent and accessible algorithms for machine learning, and to provide free machine learning tools for anyone interested in this field.

Shogun provides a well-documented Python interface for unified large-scale learning and provides high-performance speed. However, the disadvantage of Shogun is that its API is difficult to use. (Project address: Https://github.com/shogun-toolbox/shogun)

2, Keras

Keras is a high-level neural network API that provides a Python deep learning library. For any beginner, this is the best choice for machine learning because it provides a simpler way to express neural networks than other libraries. The Keras is written in pure Python and is based on the TensorFlow, Theano, and cntk back end.

According to the official website, Keras focuses on 4 key guidelines, namely user-friendliness, modularity, extensibility, and collaboration with Python. However, in terms of speed, Keras is relatively weak. (Project address: Https://github.com/keras-team/keras)

3, Scikit-learn

Scikit-learn is a Python machine learning project. 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 and random search. Its main advantage 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, network analysis, and network analysis. It also comes with complete documentation, with more than 50 examples and over 350 unit tests. The most important thing is that it is free! (Project address: Https://github.com/clips/pattern)

5, Theano

Theano is 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, defining the results you want with symbolic language, The framework compiles your program to run efficiently on the GPU or CPU.

It also provides tools for defining, optimizing, and evaluating mathematical expressions, and a number of other libraries can be built on Theano to explore their data structures. Nevertheless, there are some shortcomings in the use of Theano; For example, it may take a long time to learn its API, while others think the compilation time of the Theano large model is not efficient enough (Project address: Https://github.com/Theano/Theano)

Data Science Tools

1, SciPy

SciPy (pronounced "sigh Pie") is an open source mathematical, scientific, and engineering computing package. SCIPY uses various software packages such as Numpy,ipython or pandas to provide libraries for common mathematical and scientific programming tasks. This tool is a good choice when you want to manipulate numbers on your computer and display or publish results, and it's also free. (Project address: https://github.com/scipy/scipy)

2, Dask

The dask is a flexible parallel computing library for analytical computing. Similarly, 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 object works like NumPy can parallelize and write in pure python. (Project address: Https://github.com/dask/dask)

3, Numba

This tool is an open-source optimization compiler that uses the LLVM compiler infrastructure to compile Python syntax into machine code. The main advantage of using Numba in data science applications is that it uses the NumPy array to speed up the application's capabilities, because Numba is a compiler that supports numpy. 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 big data framework. It automatically expands the analysis/machine learning code in Python into Big data analytics and machine learning in a clustered/cloud environment, and can use the @jit adorner to optimize specific features. (Project address: Https://github.com/IntelLabs/hpat)

5, Cython

Cython is your best choice when working with code that runs in a mathematical or password loop. Cython is a Pyrex-based source code translator that quickly generates a Python extension module (extention module) tool. 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 properties. This allows the compiler to generate very efficient C code from the Cython code. (Project address: Https://github.com/cython/cython)

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10 most popular machine learning and data Science python libraries