Machine Learning - Implementing

The platform, IDE, and language for developing ML applications have to be chosen. All of these will facilitate the implementation of the AI algorithms discussed so far.

You need to understand the following aspects carefully if you are developing the ML algorithm yourself.

This essentially refers to your proficiency in a language supported by machine learning.

It depends on how familiar you are with the existing IDEs and how comfortable you feel with them.

Development platform − There are several platforms available for development and deployment. Many of these are free-to-use. However, some may require a license fee beyond a certain amount of usage. Below is a list of language choices, IDEs, and platforms you might find useful.

Language Choice

A list of languages that support machine learning is provided below

  • Python

  • R

  • Matlab

  • Octave

  • Julia

  • C++

  • C

Listed below are some popular machine learning languages. Select a language based on your comfort level, develop your models, and test them.


Listed below are some IDEs that support ML development

  • R Studio

  • Pycharm

  • iPython/Jupyter Notebook

  • Julia

  • Spyder

  • Anaconda

  • Rodeo

  • Google –Colab

As you can see from the above list, each IDE has its own advantages and disadvantages. It would be good to try a few before settling on one.


The following platforms can be used to deploy ML applications:

  • IBM

  • Microsoft Azure

  • Google Cloud

  • Amazon

  • Mlflow

This list is not exhaustive, and the reader is encouraged to try the above-mentioned services.

Frequently Asked Questions

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