2018 will be the year of rapid advances in artificial intelligence and machine learning, with experts saying Python is more down to earth than Java, making it the natural language of choice for machine learning
When it comes to data science, Python’s syntax is the closest to that of mathematics, making it the easiest language for a professional such as a mathematician or economist to understand and learn. This article lists the top 10 Python tools that are most useful for machine learning and data science applications
Five machine learning tools
1, the Shogun
SHOGUN is a machine learning toolbox focused on support vector machines (SVM) learning toolbox. Written in C++ and created as early as 1999, it is one of the oldest machine learning tools, offering a broad unified machine learning approach designed to provide transparent and accessible algorithms for machine learning, and free machine learning tools for anyone interested in this area.
Shogun provides a well-documented Python interface for unified large-scale learning and provides high performance speed. The downside of Shogun, however, is that its API is hard to use. (project address: https://github.com/shogun-toolbox/shogun)
2, Keras
Keras is an advanced neural network API that provides a Python deep learning library. This is the best choice for machine learning for any beginner, as 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 four main guiding principles, 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. Build based on NumPy, SciPy, and Matplotlib. Scikit-learn provides a consistent and easy-to-use grid of apis and random searches. Its main advantages are simple algorithm and fast speed. Scikit – learn the basic function of main is divided into six parts: the classification, regression, clustering, data dimension reduction, model selection and data preprocessing (address: https://github.com/scikit-learn/scikit-learn)
4, the 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 good documentation, more than 50 examples and more than 350 unit tests. 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 was named after the wife of Pythagoras, the Greek philosopher and mathematician. Theano’s main features: Tightly integrated with NumPy, defining the results you want in a symbolic language, the framework compiles your programs to run efficiently on the GPU or CPU.
It also provides tools to define, optimize, and evaluate mathematical expressions, and a number of other libraries can be built on Theano to explore its data structures. However, there are some drawbacks to using Theano; API such as learning it may need a long time, while others think Theano big model it is not efficient as the compilation time (project address: https://github.com/Theano/Theano)
Five data science tools
1, the SciPy
SciPy (pronounced “Sigh Pie”) is an open source math, science, and engineering computing package. SciPy provides libraries for common mathematical and scientific programming tasks using various software packages such as NumPy, IPython, or Pandas. This tool is a great choice 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 computation. Also, by changing only a few lines of code, you can quickly parallelize existing code because its DataFrame is the same as in Pandas, and its Array objects work similarly to NumPy and can be parallelized and written in pure Python. (project address: https://github.com/dask/dask)
3, Numba
This tool is an open source optimized 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 its ability to use NumPy arrays to speed up applications, since Numba is a Numpy-enabled compiler. 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 compilers based framework for big data. It automatically extends analysis/machine learning code in Python to big data analysis and machine learning in clustered/cloud environments, and can optimize specific functionality using the @JIT decorator. (project address: https://github.com/IntelLabs/hpat)
5, Cython
Cython is your best bet when using mathematical passwords or code that runs in a password loop. Cython is a Pyrex-based source code translator that quickly generates Python extention 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)