Personal tools

AI and ML in Python

[(ETH - Zurich) - Gian Marco Castelberg]


- Overview

R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.

Python is a programming language that is preferred for programming due to its vast features, applicability, and simplicity. The Python programming language best fits machine learning due to its independent platform and its popularity in the programming community. 


- R

R was developed by statisticians and basically for the statisticians which any developer can predict the same by looking at its syntax. As the language contains mathematical computations involved in machine learning which is derived from statistics, R becomes the right choice who wants to gain a better understanding of the underlying details and build innovative. 

If your project is heavily based on statistics then R can be considered as an excellent choice for narrowing down your projects which requires one-time dive into the dataset. For instance - if you like to analyze a corpus of text by deconstructing paragraphs into words or phrases to identify their patterns then R is the best choice.


- Python

The Python programming language was developed in the late 80s and plays a crucial role in powering the internal infrastructure of Google. Python comprises of enthusiastic developers and now it’s been used in the widely used applications of YouTube, Instagram, Quora, and Dropbox. Python is broadly utilized over the IT business and permits simple effort of collaboration inside development groups. In this way, in the event that you need an adaptable and multi-reason programming language with a supporting enormous network of engineers alongside the extendable AI bundles then Python is a top pick.

Machine learning is a section of Artificial Intelligence (AI) that aims at making a machine learn from experience and automatically do the work without necessarily being programmed on a task. On the other hand, Artificial Intelligence (AI) is the broader meaning of machine learning, where computers are made to be receptive to the human level by recognizing visually, by speech, language translation, and consequently making critical decisions.


- Advantages of Using Python

  • Independence across platforms -- Due to its ability to run on multiple platforms without the need to change, developers prefer Python, unlike in other programming languages. Python runs across different platforms, such as Windows, Linux, and macOS, thus requiring little or no changes. The platforms are fully compatible with the Python programming language, which means that there is little to no need for a Python expert to explain the program’s code. The ease of executability makes it easy to distribute software, allowing standalone software to be built and run using Python. The software can be programmed from start to finish using Python as the only language. It is a plus for developers since other programming languages require complementation by other languages before the project is fully completed. Python’s independence across platforms saves time and resources for developers, who would otherwise incur a lot of resources to complete a single project.
  • Consistency and simplicity -- The Python programming language is a haven for most software developers looking for simplicity and consistency in their work. The Python code is concise and readable, which simplifies the presentation process. A developer can write code easily and concisely compare it to other programming languages. It allows developers to receive input from other developers in the community to help enhance the software or application. The simplicity of the Python language makes it easy for beginners to master it quickly and with less effort as compared to other programming languages. Also, experienced developers find it easy to create stable and reliable systems, and they can focus their efforts on enhancing their creativity and solving real-world problems using machine learning.
  • Frameworks and libraries variety -- Libraries and frameworks are vital in the preparation of a suitable programming environment. Python frameworks and libraries offer a reliable environment that reduces software development time significantly. A library basically includes a prewritten code that developers can use to speed up coding when working on complex projects. Python includes a modular machine learning library known as PyBrain, which provides easy-to-use algorithms for use in machine learning tasks. The best and most reliable coding solutions require a proper structure and tested environment, which is available in the Python frameworks and libraries.


- Disadvantages of Python

  • Includes a very few statistical model packages.
  • Due to the presence of the Global Interpreter Lock (GIL), threading in Python becomes tricky and quite problematic. Subsequently, multi-threaded CPU-bound applications act slower than single-thread ones. An AI undertaking is more valuable for executing multiprocessing instead of utilizing multithreaded programming.


[More to come ...]



Document Actions