Python - The Best Programming Language for AI and ML

Artificial intelligence (AI) and machine learning (ML) let applications hear, see and process requests. Python is a flexible, stable language with a broad set of tools for this work. Go Wombat has deep Python expertise, in backend development as well as in AI applications.

Artificial Intelligence and Machine Learning in Business

AI and ML process and analyse large amounts of data. Their predictions help companies improve processes, work more efficiently and cut costs, which is why they are used across many industries.

Python – The Best Programming Language for AI and ML

These are the reasons we consider Python the best language for AI and ML projects:

Python is Easy and Consistent

Python code is short and readable. AI and ML involve complex algorithms and workflows, and readable code makes models easier to build, review and maintain. Python is also easy to learn.

Python is Flexible

Python works well with other languages: heavy maths runs in C/C++ libraries such as NumPy, while developers write the logic in Python. Because Python is interpreted, developers can change code and rerun experiments without a compile step, so they see results faster.

Python has Access to Many Libraries and Frameworks

Implementing AI and ML algorithms from scratch takes a lot of development time. Python has a large set of ready-made libraries and frameworks, including:

• TensorFlow — an open-source library for numerical computation and large-scale machine learning
• Keras — a high-level neural network API; Keras 3 runs on top of JAX, TensorFlow or PyTorch
• PyTorch — a deep learning framework widely used in research and production
• Scikit-learn — a library for classical machine learning algorithms and data preprocessing
• Pandas — a data analysis and manipulation library
• NumPy — a foundational package for numerical computing
• Matplotlib — a visualisation library for creating charts and graphs
• NLTK / spaCy — libraries for natural language processing tasks

Platform Independence

A machine learning project written in Python runs on Linux, Windows and macOS, and moving code between them usually needs few changes. This saves time and money when you test on several systems.

Broad Community of Developers

Python has a large developer community. Documentation is easy to find, most problems have already been discussed on forums, and as an open-source language Python offers plenty of tools and learning resources for developers of every level.

Python for Machine Learning and Artificial Intelligence examples

Companies in many industries build AI and ML products with Python. Some well-known examples:

Financial Technology

In finance, AI is used for risk management, automation, personalised banking and fraud prevention. Python is part of the tech stack at fintech companies such as Affirm (a buy now, pay later lender), Venmo (PayPal’s peer-to-peer payments app) and Robinhood (a brokerage platform). Venmo also built a social network into its app.

Transport

Uber built PyML, a Python-based extension of its Michelangelo machine learning platform that gives users more flexibility. Users can validate models with PyML and then move them to Michelangelo for greater capacity and scalability.

Healthcare

Healthcare AI and ML projects help predict and detect illnesses and injuries. Fathom, for example, is a natural language processing system that analyses electronic health records to automate medical coding.