Python AI: Why Is Python So Good for Machine Learning?
Favored for applications ranging from web development to scripting and process automation, Python programming is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.
AI has created a world of opportunities for application developers. It allows Spotify to recommend artists and songs to users or Netflix to know what shows you’ll want to see next. It is also used extensively by companies in customer service to drive self-service and improve workflows and employee productivity.
Python libraries are crucial in building a foundation for machine learning and artificial intelligence. Mastering these libraries prepares learners for advanced data science by enabling them to organize and analyze large data sets effectively, ultimately enhancing decision-making processes.
But what is it about Python that makes it so good for AI? In this article, we’ll take a look at the main reasons why Python is the go-to programming language for developers working in the fields of machine learning and deep learning and why you should consider it for your next AI project.
What is the difference between AI, machine learning, and deep learning?
Before we start, it might be helpful to understand the difference between AI, machine learning, and deep learning. In simple terms, deep learning is a subset of machine learning, and AI is the general category that contains machine learning.
AI is any intelligence exhibited by a machine that leads to an optimal or suboptimal solution, given a problem. Machine learning then takes this a step further by using algorithms to parse data points, and learn from them to make informed decisions.
Deep learning functions similarly, but has very different capabilities; namely the ability to draw conclusions in a manner that resembles human decision-making. It does this by using a layered structure of algorithms inspired by the neural network of the human brain. The result is a model that can learn multiple levels of representation that correspond to different levels of abstraction.
So what makes Python such a good choice for AI?
Python has become the dominant programming language in Artificial Intelligence and Machine Learning, and for good reason. Its versatility, ease of use, and extensive library ecosystem make it the go-to choice for data scientists, AI researchers, and machine learning practitioners. Mastering Python programming fundamentals is crucial as they provide a strong foundation for integrating AI tools and applying Python across various domains. This article will explore the key reasons for Python’s popularity and highlight some libraries that empower AI and ML endeavors.
Extensive selection of libraries and frameworks
One of the aspects that makes Python such a popular choice in general, is its abundance of libraries and frameworks that facilitate coding and save development time. Understanding how to write and interpret Python code is crucial for leveraging these tools effectively. Machine learning and deep learning are exceptionally well catered for.
Data structures play a vital role in building AI applications, providing the foundational programming knowledge necessary for creating practical AI tools and extracting insights from various forms of data.
NumPy, used for scientific computation, SciPy for advanced computation, and scikit-learn for data mining and data analysis, are among the most popular libraries, working alongside such heavy-hitting frameworks as TensorFlow, CNTK, and Apache Spark. In terms of machine learning and deep learning, these libraries and frameworks are in essence Python-first, while some, like PyTorch, are written specifically for Python.
Here are some of the most influential libraries and frameworks, categorized by their primary function:
Data Analysis
NumPy: The foundation of numerical computing in Python, NumPy provides robust support for multi-dimensional arrays and matrices. Its mathematical capabilities and C-based code ensure efficient data manipulation and analysis, especially for large datasets. NumPy enables users to perform various analyses, including linear algebra and multi-dimensional analysis, with its powerful array structures and functions.
Pandas: Renowned for its ability to manipulate and analyze large datasets with ease. It offers a fast, powerful, and flexible framework for data cleaning, handling missing data, and transforming data frames into formats suitable for analysis. Supported by an active community, Pandas is a cornerstone of data science workflows.
SciPy: Builds upon NumPy, offering additional modules for scientific computing. It enhances NumPy’s capabilities with tools for interpolation, solving algebraic equations, and conducting complex mathematical analyses. SciPy’s extensive range of mathematical functions makes it a critical component for any data science project involving sophisticated data analyses.
Gensim: A framework designed specifically for semantic analysis and unsupervised topic modeling, using raw and unstructured digital (plain) text. It’s often used for discovering similarities in documents, exploring customer complaints, and detecting large-scale fraud. Plus, the Gensim Word2Vec module makes it a great framework for any machine learning processes that involve word embedding in NLP, such as document classification or processing academic publications.
Data Visualization
Matplotlib: A foundational library in Python’s visualization ecosystem, known for its versatility and extensive customization options. It allows users to create a variety of visualizations, from static plots to interactive and animated charts, all within Python scripts, IPython shells, Jupyter Notebooks, and web application servers.
Seaborn: Enhances Matplotlib by offering a high-level interface for creating attractive and informative statistical graphics. It simplifies the creation of complex visualizations, enabling data scientists to produce aesthetically pleasing charts with minimal code.
Plotly: A dynamic visualization library known for its interactive data visualizations. It supports multiple plot types, including contour plots, and allows charts to be embedded in web applications, dashboards, or shared as standalone HTML files. Plotly’s unique emphasis on interactivity makes it an excellent choice for creating engaging and detailed visualizations.
Machine Learning
Scikit-learn (sklearn): One of the top NLP frameworks that offers an easy way of implementing regression, clustering, and classification for text data. Sklearn is great for classifying news articles into predefined categories, such as politics/lifestyle/sports/ etc., or analyzing newsgroup posts on different topics. Scikit-learn provides various algorithms for building ML models and intuitive classification methods. It is also precisely documented and extremely beginner-friendly, allowing engineers to see the effects of their work without having to spend too much time on it.
XGBoost: A powerful machine-learning library known for its performance in predictive modeling tasks, particularly with structured or tabular data. It has gained popularity for its effectiveness in Kaggle competitions. Its features, such as gradient-boosted decision trees and parallel tree boosting, make it highly efficient for machine learning tasks.
LightGBM: Designed to handle large datasets and high-dimensional feature spaces effectively. It optimizes for high performance with low memory consumption, making it efficient for large-scale data tasks. LightGBM uses gradient-boosting algorithms based on tree methods, ensuring robust performance for machine learning models.
Deep Learning
TensorFlow: An open-source platform tailored for machine learning. It also provides end-to-end capabilities for deep learning. It supports computations using tensors and differentiable programming, allowing for automatic derivative computations. TensorFlow’s robust community support ensures continuous development and access to extensive resources. GPU acceleration in TensorFlow significantly speeds up the training and inference processes for deep learning models.
PyTorch: An open-source deep learning framework known for its flexibility and ease of use.
Keras: (Not explicitly mentioned in the provided documents but commonly used) Keras is a high-level API for building and training neural networks. It can run on top of TensorFlow, PyTorch, or other backends, providing a simplified interface for deep learning tasks.
Natural Language Processing (NLP)
SpaCy: Considered to be the fastest NLP (and NLP-only!) framework in existence. It comes with a lot of pre-trained models to solve many problems straight out of the box. It can be used, for example, to identify money entities in news articles and extract both the value and the currency from the text. SpaCy is also pretty easy to learn for newcomers, as it requires just a single highly optimized tool for every task.
CoreNLP: Primarily written in Java, but it’s also accessible through multiple Python wrapper libraries, created and maintained by the Python community. CoreNLP integrates natively with other NLP libraries developed by Stanford and makes a great foundation for building microservices because it can be run as a web service. It’s a fast annotator for arbitrary text, used a lot in production. On top of that, CoreNLP provides very accurate techniques for tagging and parsing.
The simplicity
Python is renowned for its concise, readable code, and is almost unrivaled when it comes to ease of use and simplicity, particularly for new developers. Understanding Python basics is essential as it lays the foundation for more advanced topics and practical applications. This has several advantages for machine learning and deep learning.
Data types are also significant in Python programming. They help categorize values and perform operations, which is crucial for data analysis.
Python’s simple syntax means that it is also a faster application in development than many programming languages, and allows the developer to quickly test algorithms without having to implement them.
In addition, easily readable code is invaluable for collaborative coding, or when machine learning or deep learning projects change hands between development teams. This is particularly true if a project contains a great deal of custom business logic or third-party components.
Abundance of support
Python is an open-source programming language supported by many resources and high-quality documentation. It also boasts a large and active community of developers willing to provide advice and assistance through all stages of the software development process.
Python vs. Other AI Languages
While several programming languages can be used for AI and machine learning, Python continues to dominate due to its simplicity, flexibility, and rich ecosystem of AI-specific libraries. Structured data is essential in AI applications, and Python's libraries are particularly adept at handling and analyzing such data. Let’s explore how it compares to other popular AI languages.
Python's extensive libraries and frameworks, such as TensorFlow and scikit-learn, make it a powerful tool for developing AI models. Data preparation is a crucial step in this process, as it transforms raw data into structured information, optimizing machine learning models and enhancing their performance.
Python vs. R
R is widely used for statistical computing and data visualization, making it a strong choice for research and academic applications. However, when it comes to building AI-powered digital products, Python offers greater versatility.
With Python, developers can not only analyze data but also handle various data types, such as numerical, categorical, and ordinal, which are crucial for data analysis. Additionally, Python allows developers to process large datasets, create web scraping tools, and build deep learning architectures—all with minimal code. Its seamless integration with existing workflows makes it the better option for developing end-to-end AI solutions.
Python vs. C++
C++ is known for its high execution speed and efficient memory management, making it a great choice for performance-critical AI applications, such as real-time systems or embedded AI. However, it comes at a cost: steeper learning curves and increased development time. Python, on the other hand, prioritizes developer productivity, allowing for faster prototyping and easier debugging.
Strengthening Python skills is crucial as it not only enhances proficiency but also enables the creation of impactful AI-powered applications. While C++ is ideal for applications requiring extreme optimization, Python remains the preferred choice for most AI and machine learning tasks due to its readability and extensive library support.
Python vs. Java
Java is widely used in enterprise-level AI applications, particularly in large-scale systems where performance and scalability are key. It offers strong multi-threading support and is commonly found in AI-powered banking, fraud detection, and supply chain optimization. However, Java’s verbose syntax makes it less suited for rapid experimentation. Python programming, with its concise and readable code, allows researchers and developers to quickly build, test, and deploy AI models without unnecessary complexity. Understanding Python programming fundamentals enables users to tackle real-world problems across various domains and facilitates complex applications in machine learning through its extensive libraries and user-friendly syntax.
Wrapping up
AI is having a profound effect on the world we live in, with new applications emerging all the time. Smart developers are choosing Python as their go-to programming language for the myriad benefits that make it particularly suitable for machine learning and deep learning projects.
Python's extensive selection of machine learning-specific libraries and frameworks simplifies the development process and cuts development time. Python's simple syntax and readability promote rapid testing of complex algorithms and make the language accessible to non-programmers. It also reduces the cognitive overhead on developers, freeing up their mental resources so that they can concentrate on problem-solving and achieving project goals. Finally, the simple syntax makes it easier to collaborate or transfer projects between developers. Python also boasts a large, active community of developers happy to offer help and support, which can be invaluable when dealing with such complex projects.
While other programming languages can also be used in AI projects, there is no getting away from the fact that Python is at the cutting edge, and should be given significant consideration. This is why you should consider Python for your AI project.
