Why Python is the Go-To Language for AI and ML

Artificial Intelligence and Machine Learning or AI and ML for short, are technologies that are incredibly fast-moving and are making a greater impact everywhere, powering a lot of industries all over the world from transportation, healthcare, finance, and many more. AI and ML are all about building machines that can do something intelligent without human intervention, for instance, if a machine can recognize your face, unsurprisingly, you can say the machine is quite intelligent, at least for recognizing faces. But today's AI is really powerful, and it can do more things than just recognize faces, It can do speech synthesis, can understand text, can communicate with us, and even be our personal assistant. 

For developing this kind of system, we require powerful supercomputers, a lot of data, and most importantly a programming language that has greater support and influence, and that's where Python steps in. Python is more popular for its ability to use literally everywhere since it's multipurpose, but today Python is very popular in the field of AI and ML, and most developers are preferring it for building AI systems. In this article, we are clarifying why this is the case, and why Python is the go-to language for AI and ML? So let's dive in.

Let's get straight to the point. Python has a lot of advantages when comes to AI and ML, here are some of the advantages,

Someone who works with python at least once can even know how simple it really is. Python has a simple, intuitive syntax that makes it easy to learn and use, even for beginners. When comes to AI, simplicity, and readability plays an important role, since algorithms in AI and ML are very complex, so it needs to be implemented in a clear and understandable manner with simple structures rather than focusing on syntax and semantics.

Python has a large ecosystem of libraries and frameworks for AI and ML. Moreover, it has a large community of developers who are frequently creating and updating libraries and frameworks for AI and ML. These libraries and frameworks provide pre-built functions and modules that can be easily integrated into a project, saving time. Using these libraries, even a person with no coding background can even do machine learning with little effort.

Python is a versatile programming language that can be used for a variety of tasks, including web development, data analysis, and of course, AI and ML. This versatility helps in doing a lot of things like collecting data from online resources using web scraping for the training of AI algorithms.

Another biggest advantage of Python is that it is open-source, meaning that, everyone in the world can use it for free without any charges. This is really important because some of the development in the field of AI and ML is through startup companies. So small startups, don't need to pay for a programming language to develop AI systems when they are using python.

Python's community support is incredible, There is always someone who can help you when you encountered any issues. A lot of tutorials and resources are also there, which help you to learn and implement your own projects. Interestingly, most of the tutorials on AI and ML over the internet are using Python.

Pre-trained models are those AI algorithms that are trained by default and we don't need to train them again. These pre-trained models are very useful when you want to build AI software without many resources like computational power and data and can save more time. Fortunately, Python libraries for AI and ML have lots of pre-trained models for different types of tasks such as classification, Natural Language Processing(NLP), Speech recognition, Image recognition, etc.

Python has countless libraries and frameworks for AI and ML, discussing all of them would be beyond the scope of this article, but, let's discuss some of the popular ones

Numpy: NumPy according to the StackOverflow survey in 2022 is one of the most used libraries in the world. It stands for "Numerical Python" and it is a library, that supports numerical scientific computing and provides support for large, multi-dimensional arrays and matrices of numerical data, as well as a large collection of mathematical functions, to operate on these arrays. It is a powerful library that is widely used for scientific computing, data analysis, and machine learning tasks.

Pandas: Pandas is a great library that provides easy-to-use data structures and data analysis tools. It is built on top of NumPy and it provides two main data structures: Series (1-dimensional) and DataFrame (2-dimensional). These data structures allow you to store and manipulate data in a way that is similar to working with data in a spreadsheet. The library also provides a wide range of functions for data cleaning, manipulation, and analysis, making it a powerful tool for data preparation and feature engineering for Machine Learning tasks. 

TensorFlow: TensorFlow, developed by Google is one of the widely used frameworks for Machine Learning and Deep Learning. It supports a wide range of tools for building Neural networks and other types of algorithms that can be evaluated in real-time. It supports APIs for building custom models and also supports distributed computing, which allows developers to train models on multiple machines or GPUs.

PyTorch: Another library developed by Facebook, allows to build and train machine learning models. It helps to build complex algorithms like CNN, RNN, GANs, etc within some simple lines of code. It also supports distributed computing, so that it can run on multiple machines or GPUs.

Scikit-learn: A popular library that is used literally everywhere in machine learning and data science. It provides a large number of mathematical and scientific functions and tools for building and evaluating ML models. It also has different kinds of prebuild algorithms that can be used and trained as per our requirements. Also, it supports a lot of functions that help to do data cleaning and pre-processing. It is built on top of NumPy and SciPy, and it is designed to be easy to use and understand and implement.

Keras:  A high-level deep-learning library runs on top of TensorFlow, and it provides a simple and easy-to-use interface for building and training deep-learning models. It is designed to be user-friendly and modular, and it supports a wide range of layers, optimizers, and activation functions.

Theano:  Theano is another library for machine learning that provides a wide range of tools and functions for building and evaluating models. It is particularly useful for building deep learning models, and it supports distributed computing. Theano also allows performing calculations on GPUs.

Data pre-processing: One of the most common challenges in AI and ML is preparing and cleaning the data before it can be used to train a model. This can include tasks such as handling missing data, removing unwanted noise from data, and normalizing the data. Python libraries such as pandas and NumPy provide a wide range of functions for data pre-processing, making it easier to prepare the data for use in a model.

Evaluation: Evaluating the performance of a model is an important step in the AI and ML process. It can make sure that the model we built is working well in real-life unseen data. Python libraries such as sci-kit-learn and TensorFlow provide a bunch of tools for evaluating models, including functions for calculating accuracy, precision, and recall, and for creating confusion matrices.

Hyperparameter tuning: Another important aspect of ML is to tune the hyperparameters of a model, which are parameters that are not learned from the data, but are set by the user. Python libraries such as sci-kit-learn, TensorFlow, and Keras provide functions for hyperparameter tuning, which makes the process of finding the best combination of hyperparameters easier.

Deployment: Deploying a model to a production environment is a common challenge in ML. Python libraries such as TensorFlow, PyTorch, and Keras provide functions and tools that make it easy to export a model, and serve it in a web service or run it in other environments such as mobile, IoT, and embedded systems.

Now when comes to the conclusion, you understood why Python is the Go-To language for AI and ML. 

The simplicity and readability of Python's syntax, the vast ecosystem of libraries and frameworks, and its ability to handle a wide range of tasks make it an ideal choice for AI and ML projects.

We have also seen that Python provides libraries and frameworks that can help address common challenges in AI and ML such as data pre-processing, model selection, evaluation, hyperparameter tuning, and deployment. These libraries and frameworks, such as TensorFlow, PyTorch, sci-kit-learn, and pandas, provide pre-built functions and modules that can be easily integrated into a project, saving time and effort.

Whether you are a beginner or an experienced developer, Python is a great choice for AI and ML development. We encourage you to start experimenting with Python and its libraries and frameworks for AI and ML and to continue learning about this fascinating and ever-evolving field. There are many resources available online to help you get started, and the community of Python developers is always willing to help, including me. 

So if you have any queries, please provide them in the comment box, Thanks for reading!