Python for Machine Learning

In the past blogs, we have taken a deep dive into the world of machine learning exploring what ML is, why it matters, the different types and styles of ML systems, different applications and examples of ML in our daily life and the real world challenges ML engineers or data scientist often face. Now that we have understood what and why, its time to focus on the bigger and the most important picture of “how”. The first step in building machine learning solution is choosing the appropriate programming language.

Machine Learning is reshaping industries from voice assistants to self-driving cars and medical diagnostics. At the core of most ML systems lies a programming language that makes all this innovation accessible: Python. In today’s blog, let’s talk about Python , the language that has become the backbone of modern ML workflows and why Python is so popular for machine learning?

History of Python

Before Python became the heart of Machine Learning and Data Science, it had quite a story. In the late 1980s, Dutch programmer Guido van Rossum was working on a project in Netherlands. He was frustrated with the complexities of the existing programming languages, so he decided to create a language that was simple, readable, beginner friendly also is powerful at the same time.

He began his work in December 1989 and released the first version of Python (0.9.0) in 1991. The first version of the python already had advanced features like functions and exception handling, core data types (like lists, strings, and dictionaries), Modules and basic I/O functions.

Fun Fact: The name “Python” was inspired not by the snake, but by “Monty Python’s Flying Circus”, a British comedy show Guido enjoyed. He wanted a name that was short, unique, and slightly mysterious.

Python gained steady popularity through the 1990s, but the real boost came with Python 2.0, released in October 2000. It had key features list comprehensions, garbage collections via reference counting and unicode support. However, as Python grew, the design inconsistencies and limitations were found. It became clear that a more robust version was needed.

The big shift happened in December 2008 , when Python 3.0 was launched which is being used till date. It is a cleaner, more consistent, and future-focused version. It fixed many old design flaws but was not backward compatible with Python 2. Python 3 has better unicode handling facilities, has print() as a function and has enhanced library structure. The transition from Python2 to Python3 was slow, but eventually, Python 2 reached its end on January 1 2020, making Python 3 the official future.

As of today in 2025, Python ranks consistently in the top 3 programming languages globally. It powers everything from web apps to , AI models, robotics, and even space tech. It is maintained by the Python Software Foundation (PSF) and it continues to evolve. The most recent version is Python 3.12, with new features like faster performance and improved error messages.

Why Python for Machine Learning?

Python may have started as a general purpose scripting language, but its core principles like readability, simplicity, and flexibility making it perfectly suited for modern challenges. One such challenge is Machine Learning. In a field which often feels overwhelming , Python brings simplicity and clarity. Let’s now explore why Python has become the undisputed leader in the machine learning landscape

1. Simple and Readable Syntax

   Python code is often described as “executable pseudocode.” It feels natural to write and easy to understand which is a crucial advantage when you are dealing with complex machine learning logic. This simplicity allows ML engineers to focus on the main problem rather than worrying about syntax.

2. Rich Set of Libraries

   Python has incredible collection of libraries that are specifically designed for machine learning and data science like Numpy, Pandas, Matplotlib, Seaborn, Sci-kit learn,Tensorflow, Keras, Pytorch, OpenCV. These libraries and tools allow developers to build models and analyze the data more faster and efficiently.

3. Large Community and Beginner Friendly Documentation

   Python has vast and active global community. Stack Overflow has hundreds of thousands of ML questions answered. GitHub hosts thousands of open-source ML projects. Unlike other documentations, python has its documentation designed to be beginner friendly.

4. Integration with ML Platforms and Tools

   Python works seamlessly with tools like Jupyter Notebook, Google Colab , Anaconda for interactive coding for ML and data science problems. Also it integrates easily with C/C++ backends for performance heavy tasks.

5. Versatility across ML Lifecycle

   Python can handle Data preprocessing, Model building ,Training and tuning, Evaluation Deployment via REST APIs, Flask. It is a one stop solution for ML development.

6. Free and Open Source

   Python is completely free to download, use, and modify. It’s licensed under the Python Software Foundation License, making it open-source and community-driven. Whether you are a student experimenting with models or a researcher deploying large scale models, Python gives you full freedom without any hidden costs. Also since it is open source you can contribute to its development.

7. Cross Disciplinary Action

   Researchers, engineers, analysts, and even non-CS professionals use Python. Its syntax and ecosystem bridges the gap between academic theory and real-world applications.

Why Not Python?

While Python is widely used and loved, it does come with certain limitations. In certain scenarios, it might not be the best fit especially when speed and resource optimization matter. Here are some reasons why Python falls short in machine learning applications:

1. Slower execution speed

   Python is an interpreted language, not a compiled one like C++ or Java. This often results in slower runtime performance and could generate delay in real time systems. It creates problem in heavy computational loops.

   Many Python ML libraries like NumPy and TensorFlow are actually built on C/C++ internally to boost performance. However the core Python remains slow for CPU heavy tasks.

2. High memory usage

   Python’s memory management is not efficient enough especially for large datasets, or image/audio processing. This can cause issues on devices with limited RAM or when you are scaling machine learning pipelines.

3. Concurrency and Parallelism Issues

   Python’s Global Interpreter Lock (GIL) allows only one thread to execute at a time per process, even on multi-core CPUs. This allows only one thread to execute Python Bytecode at a time. Due to GIL, true multithreading is restricted in Python. Also it is hard to fully use multiple cores for parallel ML preprocessing or real time inference. This does not affect all use cases (e.g., I/O-bound tasks like web scraping still work fine), but for performance heavy tasks where parallel ML workloads are required , GIL can prove to be a limiting factor.

4. Deployment Challenges in some environments

   Python has very limited support when it comes to building or deploying mobile apps. Languages like Java, Swift, or even C++ are better suited for mobile development. Additionally, deploying Python applications in enterprise environments can sometimes be tricky due to dependency issues and environment setup issues.

   

The Main Strength of Python: Its Rich Set of Libraries

One of the biggest reasons Python dominates the machine learning world is its rich collection of libraries that simplifies complex tasks. Whether it’s handling large datasets, building predictive models, visualizing results, or working with text and images, Python is ready for your job. This extensive set of libraries allows both beginners and experts to focus on solving problems rather than writing everything from scratch. Below is a quick overview of some important libraries you will encounter in your machine learning journey.