Is Python a Good Language for Machine Learning?

By James Tredwell on January 26, 2019

Nowadays, Python which is a general-purpose high-level programming language has been increasingly used in machine learning algorithms. It is usually favored for applications varying from web development to scripting and process automation.

Willing to start a small business or already working in an MNC, Python provides a huge list of benefits for all. The usage of Python cannot be limited to only one activity as its growing popularity has allowed it to enter some of the most popular and complex processes like Artificial Intelligence, Machine Learning, Data Science, Natural Language Processing and much more.

Machine learning is the technology to parse data by using algorithms and also make decisions based on it. It is a field of Artificial intelligence that uses statistical techniques for the ability to learn from data given by computer systems without being explicitly programmed.

What is Python?

Python is a high level, interpreted and an OOPs based interpreted programming language. It is considered as robust and highly useful being focused on rapid application development(RAD). It works well by connecting existing components together and because of easy leaning, it has become one of the fastest growing languages in terms of scalability and adaptability. Also, the broad support and constant evolving libraries make Python a better choice for any project be it WebApp, Mobile App, IoT, ML, Data Science or AI.

Is Python a good for Machine Learning?

The answer is definitely yes. What makes Python stand out is its exceptional qualities which undoubtedly allows developers to opt it in machine learning.

#1. Expansive selection of libraries and frameworks :- Python accords for a variety of specific libraries used for scientific computation and data analysis. These libraries have been upgraded continuously to reduce the constraints that developers faced before. Here are a few important ones from many libraries to perform data analysis.

#2. Matplotlib :- A 2D plotting library which offers data visualizations in the form of histograms, power spectra, bar charts, and scatter plots.

#3. NumPy :- To perform scientific computing by assorting high-level mathematical functions to operate on multi-dimensional arrays and matrices.

#4. Pandas :- Delivers data structures and operations to change numerical tables and time series which is developed on top of NumPy.

#5. SciPy :- Offers effective routines for numerical integration and up-gradation in association with NumPy arrays.

#6. Easy to Learn :- Python is considered as the popular data analysis tool which is ahead of SQL and SAS. The developers are rushing towards the Python for easy to learn and code by promoting an easy-to-understand syntax when compared other data science languages like R and leads to a shorter learning curve. To help data scientists and engineers work in a collaborative manner, a tool names Jupyter is used for writing code and text within a web page’s context.

#7. Scalable :- Python is best known for its simple, concise and readable coding properties. As machine learning relies on complex algorithms developers have to worry less about convolution of the coding. Python is faster to use than Matlab and Stata by emerging as a scalable language. It is also utilized by YouTube for flexible problem-solving situations.

#8. Less Code :- Machine Learning involves a lot of algorithms which provides ease of testing with the help of Python. It also helps in easy writing and execution of codes to implement the same logic with the same logic as other OOPs languages. Python’s interpretation approach enables to check code methodology.

#9. Platform-Oriented :- Python comes up with the flexibility to provide an API from its existing language which itself provides extreme flexibility and becomes platform independent. You can get your application running in a new OS or other platforms just by making a few changes in codes. This approach helps to save the developers time in testing on different platforms and gets rid of code migration.

#10. Boatload of support :- As python is an open source programming language, it has a wide number of developers who are ready out there to support and guide through it. Python comes with a huge community of developers with a host of resources which are available to speed up their tasks. There is a huge community of active coders who are willing to help programmers from their early stage of developing cycle.

Wrap Up

We can conclude Python as an easy, simple, powerful, and innovative language because of its broader usage in a variety of contexts which are not associated with data science. Understanding Python and using it as per its respective strengths can refine you as a machine learning programmer which should be versatile and stay at the top of every problem. In the end, it is concluded from the features that nothing suits exceptionally well to handle the complexity of machine learning as the Python does. The decision-making ability derived from machine learning and reduced complexity of Python makes a perfect match in heaven.

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