Python – A Coveted Choice For AI And Machine Learning

Python – A Coveted Choice For AI And Machine Learning

In fact, many leading companies that are leveraging the potential of this general-purpose open-source programming language include Google, Amazon, Facebook, Netflix, Spotify, PayPal, Goldman Sachs, NASA, Instagram, Pinterest, and more.

Though there are many other tech-stacks available for developing AI and Machine Learning solutions, Python has stolen the thunder with its host of libraries and frameworks. Today, it has become the most coveted choice among developers and CTOs for AI and Machine Learning (ML) development solutions. This may be one of the reasons why many businesses are actively planning to hire Python developers for technological transformation that helps them accelerate their productivity, streamline workflows, and improve ROI.

Before you partner with a Python development company and hire developers for AI and Machine Learning-based projects, it’s considered wise to understand why you should choose Python over R, Go, Scala, and other programming languages. Let’s quickly dive into the reasons.

1 A rich ecosystem of libraries and frameworks

One of the key reasons that make Python a coveted choice for AI and Machine Learning projects is that it offers developers quick access to a host of libraries. A library is a pre-written portion of code that allows developers to quickly perform common programming tasks without the need to rewrite several lines of code. In other words, Python offers base-level items to developers so that they don’t have to write multiple lines of code from scratch, which in turn, cuts down the high development time.

Some popular libraries for developing AI and Machine Learning solutions include:

Pandas: This Python library is used by developers for high-level data analysis and structures. With it, developers can not only collate and sort data but also collect the same from diverse external sources.
Matplotib: With this library, Python developers can quickly create 2D plots, charts, histograms, and related formats for data visualization.
Scikit-image: This Python library is used for image processing
TensorFlow: It is used by Python developers when working with deep learning models by developing, training, and using artificial neural networks with large datasets.
StatsModels: It is used for data exploration and statistical algorithms
Scikit-learn: Python developers use this library to manage significant Machine Learning algorithms including linear and logistic regressions, clustering, classification, regression, and more.
NLTK: This Python library is used when working with natural language recognition, computational linguistics, and processing.
Caffe: This library is used when working with deep learning and it allows developers to switch between the GPU and the CPU. It can process 60+ mln images per day by using an NVIDIA K40 GPU.