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Top Reasons Why Python is Used for Machine Learning

  • POSTED ON
  • April 27, 2023
  • POSTED BY
  • Muhammad Ahmad
  • POSTED ON April 27, 2023
  • POSTED BY Muhammad Ahmad

The future of intelligence technology is held by AI and ML. Apps and softwares are integrated with AI and ML technology and have become smarter. Improved functionalities, smarter recommendations, and better search results along with other features; software is getting smarter and more efficient day by day.

The most asked question here is what language is most suitable for AI and ML (machine learning). The short answer is Python! And to get into nitty-gritty details on why Python is used for machine learning follow along.

Why is Python the best language for machine learning?

Developing ML and AI software takes a different route than traditional software development. The first basic difference is the technology stack and tools used for development. Secondly, the skills required are also miles apart. To develop AI applications you should use a programming language that is stable, flexible, and offers tools integration. Stability and flexibility are needed because of diverse functionality and operations for development. Python is a language that offers all of the above. 

Additionally, python offers ease of development, maintenance, and deployment. Hence, it boosts productivity and reduces development time. Python for Artificial Intelligence is a great choice because of the tools, frameworks, and libraries it has dedicated for machine learning.

Let’s look at why Python for AI is the best choice.

Consistency & Simplicity

One impressive reason for using Python is its simplistic and concise code. Algorithms used for artificial intelligence are already so complex that using a complex language will worsen the situation. Python allows developers to write simple and easy-to-read code so developers can shift focus to solving problems instead.

Many AI and ML developers also chose Python because of the easy learning curve, thus they can spend time learning.

More intuitive than other languages, offering more frameworks, libraries, and tools, simplistic code, and flexible nature are a few important features of Python that provoke many developers to opt for it. 

Extensive list of libraries & frameworks

Machine learning algorithms are time taking and tricky to execute, thus structured development and developer suitable environment is necessary. For reduction in development time and to give structure to the software python offers a number of frameworks and libraries. Following is a list of popular and high-in-demand Python AI libraries and frameworks.

  • Keras, TensorFlow, and Scikit-learn for machine learning
  • NumPy for high-performance scientific computing and data analysis
  • SciPy for advanced computing
  • Pandas for general-purpose data analysis
  • Seaborn for data visualization

Scikit-learn is a library for machine learning having various features. Classification, regression, and clustering algorithms include support vector machines, random forests, gradient boosting, k-means, and others. These features are designed to work with Python AI libraries like NumPy and SciPy for better efficiency and performance. 

For development, there is a wide array of options, that also includes features like testing and debugging, and code refactoring. Hence an extensive list of Python for AI frameworks and libraries is worth investing your time in. 

Specific libraries & frameworks use

Following is the list of tasks or fields and which Python frameworks are best suited for that task. 

What is Python good for? Here’s a table of сommon AI use cases and technologies that are best suited for them. We recommend using these:

Data Analysis And VisualizationNumpy, Scipy, Pandas, Seaborn
Machine LearningTensorflow, Keras, Scikit-Learn
Computer VisionOpencv
Natural Language ProcessingNltk, Spacy

Independent and free

Python is independent of any platform, meaning the code you write on one machine can run flawlessly on another machine without any changes. Being platform-independent, python AI and ML gel together so well. From creating standalone executable programs to AI programs python software is easy to distribute and maintain. Why is Python used for machine learning, if you still have not got the answer, read more benefits.

Community support

Python is one of the most popular (top 5) programming languages. This means you can hire Python developers without any problem whenever required. In addition, having great community support is good news for developers, as they can get significant assistance from professionals around the world. 

The Python AI and ML community has grown dramatically over the last few years, as the demand for AI and ML developers increased. For any problem you face, mostly there already is a solution available, which can be found with just a little research. But, if you cannot find the solution to your problem, online forms and communities are always there to help. The amazing fact is you will get an answer well within time and custom to your problem.

Other programming languages for AI and ML

Regardless of so many AI and ML developers, there is still a shortage of resources in the industry. Even though Python is the most used programming language used for AI and ML development, others do have their fair share. Following is a list of popular and most used programming languages used and their benefits explained. 

R

R is for data analysis and manipulation, which does better than any other language. This quality is because of its mathematical and statistical power. Like any other language, R also offers a few packages like Gmodels, Class, Tm, and RODBC which are popular for building machine learning and AI projects. With these packages, you can implement ML algorithms without too much of a hassle. 

Its statistical power comes from its creation POV, as it was created for statistical analysis. For machine learning projects which require high-quality graphs and charts, R is the best choice, but only for this purpose. Turning visuals into interactive apps and giving shape to your ideas is what R is great at. 

When compared to Python, R is slower, and large-scale software starts lagging. So developers prefer using Python or Java instead.

Scala

Scala offers invaluable tools (Breeze, Saddle, and Scalalab) for data scientists and ML engineers. It can also easily process huge data chunks and complex operations. Scala can go head to head with Hadoop for data processing for large-scale applications, as it runs on JVM. Scala code is easily maintainable and highly efficient, thus it is the choice of many even though it does not offer as many tools, libraries, and frameworks compared to R and Python. 

Julia

Julia is a new addition to the list but it covers all the pitfalls of other languages and effortlessly overcomes them. It is syntactically similar to Python and fully supports deep learning with TensorFlow.jl wrapper and Mocha framework. Compared to Python it does not have community support.

Java

Java is powerful, versatile, and maintainable like Python. It is object-oriented and provides support for AI development with WEKA and Rapidminer libraries. It is best suited for NLP and neural networks enabling the rapid development of large-scale software.

Inversely, it is the least recommended language for statistical modeling and data visualization, even though it offers packages that support these. Compared to Python its tools and packages are not that advanced. 

So, to answer the question; why is Python used for machine learning, above mentioned are the benefits of using it. Overall, python is the best-suited language that provides overall support and covers all functional areas, regardless of what type of AI and ML development you want to go in. Additionally, a huge community and immense support backed with tons of tools, frameworks, and libraries take the language close to perfection.

ABOUT THE AUTHOR

Muhammad Ahmad

Currently serving as the SEO Manager at vteams, Ahmed is a highly skilled individual with several years of experience of Digital Marketing.

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