6 Excellent Python Tools for Data Science and Machine Learning

Experts have made it fairly clear that 2018 will be a bright year for machine learning and artificial intelligence. Some of them have also conveyed their view that “Machine learning inclines to have a Python flavor as it’s more user-friendly than Java”.

When we talk about data science, Python’s syntax is the closest to the mathematical syntax and, hence, is the language that is most simply understood and learned by professionals such as mathematicians or economists.

6 Python Tools for Data Science and Machine Learning

Machine learning tools                                       

                                                   

Shogun – Written in C++, Shogun is an open-source machine learning toolbox with an emphasis on Support Vector Machines (SVM) and it’s among the oldest ML tools, created in 1999! It gives a broad range of combined machine learning approaches and the objective behind its creation is to offer machine learning with transparent algorithms and machine learning tools to anyone interested in this domain.

Shogun provides a well-documented Python interface and it is generally designed for integrated large-scale learning and gives a high-performance speed. Though, some find its API tough to use.

Pattern – Pattern is a web mining module which provides tools for data mining, network analysis and visualization and machine learning. It comes with well-documentation and more than instances as well as above 350 unit tests. And most outstandingly, it’s free!

Keras – It is a high-level neural networks API and offers a Python deep learning library. It is the best option for any beginner in machine learning as it provides an easier way to represent neural networks as compared to other libraries. Written in Python, Keras is capable of running on top of famous neural network frameworks such as TensorFlow, CNTK or Theano.

Data science tools

SciPy – It is a Python-based ecosystem of open-source software for science, engineering and mathematics. It uses numerous packages like IPython or Pandas, NumPy to deliver libraries for common math- and science-based programming tasks. This tool is an excellent option when you need to manipulate numbers on a computer system and display the outcomes and it is free as well.

Dask – Dask is a tool offering parallelism for analytics by incorporating into other community projects like Pandas, NumPy, and Scikit-Learn. With this too, you can speedily parallelize prevailing code by altering only a few lines of code, because its DataFrame is the similar as in the Pandas library, its Array object functions like NumPy’s has the capacity to parallelize jobs written in pure Python.

HPAT – High-Performance Analytics Toolkit or HPAT is a compiler-based framework for big data. HPAT automatically scales machine learning/ analytics codes in Python to bare-metal cloud/ cluster performance and can enhance certain functions with the @jit decorator.

If you wish to learn data science with Python along with data manipulation, interlacing theory and basic constructs, then you should join a Data Science with Python program through a reputed institution. This will help you gain knowledge of the domain from the scratch.