Why is Python Best Suited for Machine Learning?

What’s common between Netflix, Nasa, Google, Spotify, and Uber? The answer might surprise you – They've all used Python in machine learning! Over the years, Python has come a long way and has become a popular choice for machine learning. 
 
70% of data scientists use Python in ML due to its simplicity, efficiency, and extensive library support. Additionally, Python’s extensive libraries like TensorFlow have played an important role in AI development. 
 
What are the other reasons that makes Python a popular choice for ML? In this article, we’ll look at some main reasons, and why you should consider them in your next project. 
 
4 Key Features of Python for Machine Learning 
 
Python’s dominance in the machine learning realm, was no accident. It’s enjoyed a steady rise in the programming field and has made its mark – All for a good reason. Here are the top 4 reasons why -  
 
1) Extensive Libraries  
 
One of Python’s biggest strengths is the libraries it offers. It can help developers solve complex tasks with ease. Some of the popular libraries include - 
 
i) NumPy: A key component for data manipulation in machine learning since it supports huge, multi-dimensional arrays and matrices and is necessary for numerical computations. 
 
ii) Pandas: It’s a good choice for data processing; Pandas allows developers tools for data manipulation and analysis. 
 
iii) TensorFlow and Pytorch: These libraries act as a base for deep learning, allowing developers to quickly build and train neural networks. 
 
These libraries don’t just help simplify workflows but also provide detailed documentation for different topics. This makes it easier for developers to quickly find solutions. 
 
2) Ease of Learning and Readability 
 
Python’s code is easy to learn and read, even for beginners! Additionally, its intuitive syntax makes it popular in Python in ML. 
 
i) Beginner Friendly: Unlike other programming languages like C++ and Java, Python doesn’t require extensive coding knowledge to get started. 
 
ii) Clean Syntax: Surprisingly, Python’s syntax is easy to read and doesn’t use any complex structure and symbols. As a result, it makes it easier for teams to maintain the code quality. 
 
iii) Rapid Prototyping: Python’s simplicity allows developers to quickly test their ideas through rapid prototyping. This is essential to machine learning's iterative process. 

Related Blog: Python Features You Must Know for Your Web Development Journey in 2025
 
3) Strong Community Support 
 
Python has one of the largest community support and plays an invaluable part for Python in Machine Learning. 
 
i) Regular Updates: Get access to regular updates and features, which help improve the programming language. 
 
ii) Active Forums: For instance, Github and Reddit have communities of developers, asking questions and work on projects together.  
 
iii) Open-Source Contributions: Because Python is open-source, numerous libraries, frameworks, and tools have been developed, most of which are particularly for ML and AI. 
 
4) Cross Platform Compatability 
 
The fact that Python can run on different platforms makes it a versatile choice in machine learning. 
 
i) Support: Python is compatible with major operating systems like Windows, MacOS, and Linux. This ensures that ML models can be deployed in these systems. 
 
ii) Language Interoperability: Python is suitable with C++, Java, and other languages. This enables the usage of various programming languages in a single project by developers. 
 
iii) Cloud Integration: Python in ML also works well with cloud platforms like AWS, Azure, and Google Cloud. 
 
Python’s cross-platform functionality has made it a top choice for businesses to build end-to-end ML pipelines. 
 
Python Vs Other Languages 
 
When it comes to comparing Python in Machine Learning with other languages, how does it perform? Let’s break it down: 
 
1) Python Vs Java 
 
i) Ease of Use: Python’s code is easier to read and comprehend, whereas Java’s code is intense and requires coding knowledge. 
 
ii) Library Support: Java has limited library support as compared to Python’s larger library options. 
 
iii) Speed: Although Java is faster, Python’s extensive libraries and frameworks compensate for the speed. 
 
2) Python Vs R 
 
i) Purpose: R is a great option for research, statistical analysis, and data visualization. Python has a broader purpose and can be used for AI and ML applications. 
 
ii) Community: Python wins again, due to its wider community support as compared to R’s community support. 
 
iii) Integration: Python works well with other tools and platforms, making it an ideal choice for ML pipelines.  
 
3) Python Vs C++ 
 
i) Performance: C++ is faster and suitable for critical projects that prioritize performance. However, Python’s rapid prototyping makes it easier to build ML projects. 
 
ii) Development: C++ requires more time to deploy a project, whereas Python has faster development speed. 
 
iii) Ease of Learning: C++ has a steeper learning curve, whereas Python is beginner friendly due to its simpler syntax. 
 
Challenges of Using Python in Machine Learning 
 
While Python for machine learning has several benefits to offer, it has a few limitations of its own. Here are the top 3 - 
 
i) Performance Issues 
 
Python is an integrated language. This means that it can be slower as compared to other programming languages like Java and C++. Additionally, training large-scale neural networks, it can lead to longer execution time. 
 
ii) Memory Consumption 
 
If you have limited hardware memory, then working with Python can be an issue. It's dynamic typing and high-level abstractions will require a high memory usage. 
 
iii) Weak in Mobile Environments 
 
Unfortunately, Python isn’t suited for mobile or browser environments. Programming languages like Java Script are best suited for these projects. 
 
Wrapping Up 
 
As technology evolves, Python has again proven to be a popular choice for machine learning projects. Although it has its own set of limitations, it makes it a great choice for businesses of all industries! 
 
If you need help in getting started, consider partnering up with Python India. Their team of developers can use Python in machine learning and take your projects to the next level. Connect with them today to know more.