The Use of Python in AI and ML

Oct 22, 2020
4 min read
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The open-source programming language Python has been around for a long time, but it continues to be a dominant force. Its popularity in the digital age is attributed to the fact that you can build many cool things with Python (like basic QA Systems).

In recent years, Python’s dominance has also extended as a result of its ability to manage complex coding scenarios, especially in the domain of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

According to O’Reilly Media, Python’s popularity was fueled by ML development. In fact, Python was the most popular coding language on the platform, accounting for 10% of all usage. O’Reilly further states that Python’s popularity on its platform was driven by data scientists, AI engineers, and ML engineers.

For example, AI is leveraged to build prediction models to engage in data analytics, create robust strategies, boost productivity, enhance efficiency, and more. All this is achieved through programming in Python.

As oceans of data are generated every second of the day, AI and ML are used extensively for its processing and analysis capabilities (a feat that can’t be achieved by the human brain because of the volume of data it can absorb at any given moment).

What Are the Key Benefits of Using Python in AI and ML?

When it comes to AI and ML, Python is like a super stack demanded by AI and ML-based experiments.

The key benefits of leveraging Python for your next AI and ML project are as follows:

  • Low barrier to entry
  • Extensive libraries
  • Enhanced flexibility
  • Consistency
  • Stability
  • Readability
  • Platform agnostic

For this post, let’s take a closer look at some of these benefits.

Low Barrier to Entry

When working with lots of data, you have to find innovative ways to process it conveniently and effectively. As Python is easy to learn, most data scientists pick it up quickly and engage in AI development without external support.

This is because Python resembles simple English with a simple syntax that allows the seamless management of complex systems. The extensive documentation available within the Python community also helps accelerate this process.

Extensive Libraries

It also helps that Python is supported by extensive libraries, structures, and thriving online communities. Python libraries are also one of the main reasons why the code has come into prominence within AI.

These libraries are collections of modules distributed across a variety of sources like PyPi. The latter is equipped with a pre-composed bit of code that enables clients to perform analytical activities. Code like this can form the foundation of AI-based projects, saving time and money.

For ML projects, Python libraries enable continuous information prepping and data processing. In this scenario, Python libraries allow you to access, manage, and transform the data seamlessly.

So when AI engineers and ML code with Python, they are sure about the product that’s built (and can move forward with confidence).

Some popular libraries used in AI and ML projects are as follows:

  • Caffe (for DL that enables seamless switching between CPU and the GPU, while processing over 60 million images a day using a single NVIDIA K40 GPU).
  • Keras (for DL, enabling rapid calculations and prototyping)
  • Matplotlib (for 2D plots, charts, histograms, and more)
  • NLTK (for processing, computational linguistics, and natural language recognition)
  • Pandas (for high-level data structures and analysis)
  • PyBrain (for neural networks, reinforcement, and unsupervised learning)
  • PyTorch (developed by Facebook to support Computer Vision, NLP, and many other ML programs)
  • Scikit-image (for image processing)
  • Scikit-learn (for managing basic ML algorithms like classification, clustering, linear and logistic regressions, regression, and more)
  • Spark MLlib (developed by Apache, enables easy scaling of your computations quickly and provides seamless integration with other tools)
  • StatsModels (for data exploration and statistical algorithms)
  • TensorFlow (for working with DL by setting up, training, and utilizing artificial neural networks with enormous datasets)

Some libraries also make excellent visualization tools. This makes it easy to represent data in a human-readable format. As mentioned above, Matplotlib, for example, allows the creation of charts, histograms, and plots to enable simple comprehension. These libraries and visualization tools also make it much easier to create reports that are easily understood by a non-technical audience.

Enhanced Flexibility

Python is highly adaptable. For example, you can use scripting or OOPs when coding. AI engineers can implement changes quickly without ever recompiling the source code. You can also combine it with other programming languages as needed (especially C and C++ code modules).

This creates a robust environment where developers choose coding styles they are comfortable with and can solve different problems in the best possible way.

For example, the commands made in the imperative style describe how a task is performed. As the program state changes, you can define the computations sequence that needs to happen with each change.

Or if ML engineers choose a functional or declarative style, the operations that need to be performed are “declared.” In this scenario, declared statements take the form of mathematical equations and don’t consider the program state.

One benefit that’s not mentioned often is Python’s ability to simplify the code and make it readable. This programming language focuses on cutting down the number of lines of code to execute a function. So when AI and ML projects are coded in Python, programmers won’t have to write a lot of code.

If you’re just starting your Python journey, you’ll soon realize that it’s a fantastic tool that is more than a general-purpose programming language. Its extensive libraries, community support, flexibility, and low barrier to entry makes it the go-to language for anyone thinking about a career in AI or data science.

Going forward, Python’s popularity also takes pressure off startups and recruiters. Whenever engineers and team members need to be replaced, it’ll be much easier to put warm bodies on seats.

It also easier for anyone in the company to quickly learn and engage in an AI or ML-based project. What’s more, the lack of a learning curve also makes Python a highly cost-effective solution.