Machine Learning - Implementing


To develop ML applications, you will have to decide on the platform, the IDE and the language for development. There are several choices available. Most of these would meet your requirements easily as all of them provide the implementation of AI algorithms discussed so far.

If you are developing the ML algorithm on your own, the following aspects need to be understood carefully −

The language of your choice − this essentially is your proficiency in one of the languages supported in ML development.

The IDE that you use − This would depend on your familiarity with the existing IDEs and your comfort level.

Development platform − There are several platforms available for development and deployment. Most of these are free-to-use. In some cases, you may have to incur a license fee beyond a certain amount of usage. Here is a brief list of choice of languages, IDEs and platforms for your ready reference.

Language Choice

Here is a list of languages that support ML development −

  • Python
  • R
  • Matlab
  • Octave
  • Julia
  • C++
  • C

This list is not essentially comprehensive; however, it covers many popular languages used in machine learning development. Depending upon your comfort level, select a language for the development, develop your models and test.


Here is a list of IDEs which support ML development −

  • R Studio
  • Pycharm
  • iPython/Jupyter Notebook
  • Julia
  • Spyder
  • Anaconda
  • Rodeo
  • Google –Colab

The above list is not essentially comprehensive. Each one has its own merits and demerits. The reader is encouraged to try out these different IDEs before narrowing down to a single one.


Here is a list of platforms on which ML applications can be deployed −

  • IBM
  • Microsoft Azure
  • Google Cloud
  • Amazon
  • Mlflow

Once again this list is not exhaustive. The reader is encouraged to sign-up for the abovementioned services and try them out themselves.

Useful Video Courses


Theory of Machines

168 Lectures 13.5 hours

Er. Himanshu Vasishta


Mathematics for Data Science and Machine Learning using R

64 Lectures 10.5 hours

Eduonix Learning Solutions


Python Machine Learning & Data Science for Dummies

91 Lectures 10 hours

Abhilash Nelson


Linear Regression Analysis in R - Machine Learning Basics

54 Lectures 6 hours

Abhishek And Pukhraj


Support Vector Machines (SVM) in Python

35 Lectures 4 hours

Abhishek And Pukhraj