6 Compelling Reasons to Choose Python for Machine Learning

Do you know why Python is most preferred when it comes to machine learning? Python is being used by several top-tier companies such as Google, Facebook, Netflix, Dropbox, Reddit, etc. 

The reason for this is that Python helps you to solve complex problems quickly and with less code, saving you time and effort in the development process.

There are more reasons to choose Python for machine learning and this article covers 6 such reasons. So, without further ado, let us get started!

Top 6 Reasons to Choose Python for Machine Learning

Let’s take a closer look at the significant features of Python, that make it so popular among machine learning developers, and answer the query of why Python is for machine learning.

1. Libraries and Framework

One of the critical reasons Python is the most common AI programming language is many libraries.

A library is a pre-written piece of code that enables users to access some features or perform various actions. It is released by multiple sources such as PyPi and includes a pre-written piece of code.

Some of the most widely used libraries for machine learning and artificial intelligence offered by Python are Scikit-learn, Pandas, Keras, Tensor Flow, Matplotlib, etc.

These Libraries facilitated various tasks, including DevOps, Web creation, data science, and computer vision which is one of the biggest reasons why Python is used for Machine learning.

Why Python’s Library?

• Continuous data processing is required for machine learning, and Python’s libraries allow you to access, handle, and transform data.
• Python’s Standard Library makes it a general-purpose programming language that isn’t restricted to specific applications. Its diverse ecosystem of modules and repositories spans a wide range of topics and industries.
• Python libraries provide basic functionalities so that developers don’t have to start from scratch every time.

2. Simple and Flexible

Python is an excellent choice for machine learning because it is a very flexible language. It gives users the choice of using Object Oriented Programming or scripting. 

There’s no need to recompile the source code, so developers can improve and see the results right away. The simplicity and flexibility of Python make it a popular one for machine learning.

Furthermore, flexibility helps developers choose the programming styles in which they are most comfortable and combine these styles to solve various types of problems in the most efficient manner. 

Nobody likes overly complex things, which is why Python’s ease of use is one of the key reasons for its popularity in Machine Learning. It is easy to learn and use, with a basic syntax that appeals to experienced programmers and students.

3. Low-entry Barrier

Working in the ML and AI industry entails dealing with a large amount of data that must be processed most efficiently and productively. Because of the low entry barrier, more data scientists will quickly learn Python and start using it for AI production without putting in a lot of effort.

Python is a programming language that is similar to everyday English, which makes learning simpler. Its simple syntax makes it easy to work with complex systems while maintaining consistent relationships between system components. The simple syntaxes also allow you to collaborate with multiple complex systems easily.

The low entry barrier also adds to Python’s status as a machine learning and data science favorite, ensuring that it will continue to exist for a long time.

4. Community & Popularity

It’s always beneficial to have a large community developed around a programming language. Python is an open-source language, which ensures that programmers at all levels, from beginners to experts, have access to a wealth of tools.

Python has a vast and welcoming developer community that is only increasing in size. Strong best practices are drafted in these communities and forums to enforce the language’s ideology.

There is a wealth of Python documentation available online and Python communities and forums where programmers and machine learning developers discuss bugs, solve problems and assist one another.

This makes it easier to find developers and substitute team members if necessary. Furthermore, the expense of their work might not be as high as it would be if they used a lesser-known programming language.

5. Code Readability

Python is very simple to read so any Python developer can understand and modify, copy, or share the code of their peers.

There are no misunderstandings, bugs, or competing paradigms, resulting in a more effective sharing of algorithms, concepts, and tools between AI and ML experts.

There are also resources available, such as IPython, an interactive shell with extra features such as checking, debugging, tab completion, and others, making the work process more straightforward. 

Machine learning entails many algorithms, which can be difficult for software developers to implement at times. Python provides a safe testing environment and allows you to write and execute code quickly and efficiently.

6. Platform Independence

Python is not only simple to use and understand, but it is also highly flexible. Python’s popularity in Machine Learning can be attributed to this. Python is used to build machine learning applications on various platforms, including Windows, MacOS, Linux, Unix, etc.

Packages like PyInstaller help developers prepare their code for various platforms. Again, this saves time and resources for experiments on different platforms while also simplifying and streamlining the overall process. 

The compact and extensible nature of Python allows it to perform a wide range of cross-language operations. Many data scientists tend to train their machine learning models on their machines using Graphics Processing Units (GPUs), and Python’s portability is ideal for this.

If you want to learn more about how Python is used in AIML, consider enrolling in GUVI’s Artificial Intelligence & Machine Learning Course where you will master technologies like matplotlib, pandas, SQL, NLP, and deep learning,  and build interesting real-life machine learning projects.

Conclusion

In conclusion, Artificial Intelligence (AI) and machine learning (ML) are rapidly evolving, and these universal technologies allow scientists to solve real-world problems and devise innovative solutions. Python is one of the most widely used programming languages in the world today.

As AI and ML become more widely used across different platforms and sectors, large companies invest in these fields, and the market for ML and AI experts rises in tandem. So, what are you holding out for? Start learning Python and become a machine learning expert!

FAQs

1. Why is Python favored for machine learning? 

Python’s simple syntax and readability make it accessible, allowing developers to focus on solving ML problems rather than dealing with complex code structures.

2. How do Python’s libraries benefit machine learning projects? 

Python offers extensive libraries like TensorFlow, Keras, and scikit-learn, which provide pre-built modules and functions, simplifying the development of machine learning models.

3. What role does Python’s community play in its machine learning applications? 

Python boasts a large, active community that contributes to a wealth of resources, tutorials, and forums, offering support and accelerating development in machine learning projects.

4. Is Python suitable for large-scale machine learning applications? 

Yes, Python’s flexibility and scalability make it suitable for both small-scale and large-scale machine learning applications, accommodating various project requirements.

5. How does Python’s flexibility enhance machine learning development? 

Python’s flexibility allows developers to choose between object-oriented or scripting approaches and integrate with other languages, facilitating the implementation of diverse machine learning solutions.