Artificial Intelligence (AI) and Machine Learning (ML) are the new beacons of hope for the IT industry. AI went far beyond science fiction and slowly becoming a necessity. Being widely used for processing and evaluating huge volumes of data, AI assists in handling the work that can’t be accomplished manually anymore because of its significantly increased volumes and intensity.
As AI and ML are applied across multiple industries and sectors, large corporations invest in these fields, and the demand for R programming help experts in ML and AI grows accordingly.
With that thought, let’s elaborate on the role of the python programming language in the successful integration of AI-powered systems.
Uses of python in AI and ML
Python comes with many features that help integrate AI and ML in specific applications, and that makes it the best language for these purposes. So, it’s not really surprising that various industries use Python for AI and other machine learning functions.
Some of the prominent names in the travel industry, like Skyscanner, used a Python unsupervised ML algorithm to predict the behaviour of new aeroplane routes. They compared thousands of origins and destinations, assessing each one of them with 30 different criteria to determine the demand of passengers. Their outcomes are displayed on a dashboard, where you can select any origin city to check the groups of destinations numbered from 0 to 9 and their characteristics.
Such instances of AI implementation in the travel industry are beneficial for suggesting destinations to the users. This, in turn, helps in the creation of marketing budgets as well as to set an initial price for new routes.
Uber came up with an ML platform Michelangelo PyML with the help of python. They use it for offline and online predictions to get through the day-to-day tasks.
The Michelangelo PyML is the extension of the actual Michelangelo product, which was scalable but not flexible enough. Users can support the models with PyML and then copy them in Michelangelo for complete scalability and efficiency.
AI used in financial services supports solving problems related to risk management, personalised banking, automation, fraud prevention, and other resources which help provide a high-quality financial service to the consumers.
Some successful examples of online banking software developed with Python are Affirm, Venmo, or Robinhood. These services not only enable users to make and take control of their payments and purchases but also present a social network inside the software, so people can stay connected.
AI is reinventing the healthcare industry by helping to diagnose and treat diseases, spot injuries, and assist people in maintaining good health even on a day-to-day basis with simple and efficient mobile applications.
There are many great AI-based solutions in this specific industry that utilise natural language processing systems. Such systems are created to assess electronic health records, and their objective is “to automate medical coding.” Some of the organisations like Amazon, Google, Facebook or the universities of Stanford and Harvard.
There are organisations that have begun using technologies like face recognition, action recognition, and pill recognition. Such apps are also able to assess the patient’s state and understand if the treatment is working. They use IMA (Interactive Medical Assistant) that can gather clinically significant data, which then can be assessed by the software.
Why choose a python programming language for integrating AI
- A great library ecosystem
A vast choice of libraries is one of the prime reasons python is the most popular programming language utilised for AI. A library is a module or a group of modules published by various sources, which include a pre-written piece of code that enables users to perform different actions. Python libraries offer the base level items, so developers don’t have to code them from scratch every time.
ML requires continuous data processing, and python’s libraries let you access, handle and transform data. Some of the most widespread libraries you can use for ML and AI include, Scikit-learn, Panda, Tensorflow, Matplotlib, Pybrain,
- A low entry barrier
Working with AI and ML means dealing with a bulk of data that you must process in the most effective way possible. The low entry barrier lets more data scientists quickly pick up python and start using it for AI development without wasting too much effort on learning the language.
Python programming language is similar to the everyday English language, and that makes the process of learning easier. Its simple syntax allows you to work comfortably with complicated systems, maintaining сlear relations between the system elements.
- Flexibility is the key
Letting developers have complete flexibility for AI applications is what Python programmers admire about the language. Python for Machine Learning allows you to opt for scripting-based programming. It enables a quick result view without completely recompiling the Python code.
There are four different styles of Python software that you can select from. There’s the imperative, functional, object-oriented, and procedural style – all of which reduce the possibility of errors based on your essay typer AI project.
AI and ML are universal technologies that are used to resolve real-life dilemmas and come up with clever solutions. Now, the python programming language comes with scalability and power to deal with massive amounts of data requests simultaneously. It’d be intriguing to see the integration of python and AI in the future.
Author bio: Stephen Anderson is a web developer for a reputable corporate firm in Australia. He has acquired his Master’s degree in Computer Science from Federation University. He holds extensive knowledge of programming languages like Python, C#, and R. He’s also a member of Essayassignmenthelp.com.au and offers university assignment help to students.