Now a days Machine Learning is most demanding and emerging technology in the world. Machine learning is subset of Artificial Intelligence.

Machine learning is use in development of AI assistant, Chat Bots, Navigation system, Image detection application, etc.

The most popular language used for Machine Learning is Python and R. Both are open source and easy to learn programming languages.

Here are some differences and comparisons between Python and R :

**Python:** Python is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991.

**R:** R Language is used for machine learning algorithms, linear regression, time series, statistical inference, etc. It was designed by Ross Ihaka and Robert Gentleman in 1993.

**Usage:**

R is suitable for statistical programming which includes statistical computing and graphics.

Python is a general purpose programming language for web development, machine learning, data analysis and scientific computing.

**Syntax:**

Python has an easy-to-read syntax.

R, on the other hand, is known for having difficult syntax.

**Optimization and Speed:**

R can perform complex computations really fast.

Being a general-purpose high level language, Python is not always the best option in terms of speed.

**IDE:**

Various popular R IDEs are Rstudio, RKward, R commander, etc.

Various popular Python IDEs are Pycharm, Spyder, Atom, etc.

**Library and packages:**

R have many packages and libraries like ggplot2, caret, etc.

Some essential python packages and libraries are Pandas, Numpy, Scipy, etc.

**Scope:**

R is mainly used for complex data analysis in data science.

Python takes a more streamlined approach for data science and ML projects.

**Integrations:**

R is also challenging to integrate in engineering environments compared to Python. Since R is limited to statistical analysis and visualization, it’s not an ideal choice for an ML program.

Python can be integrated with a large-scale environment that fulfills a range of operations.

**Example:**

`# R program to add two numbers`

num1 <- 34

num2 <- 56

# Adding two numbers

sum <- num1 + num2

print(paste("The sum is", sum))

`# Python program to add two numbers`

num1 = 18

num2 = 45

# Adding two numbers

sum = num1 + num2

# Printing the result

print("The sum is", sum)

**Conclusion:**

Python’s versatility makes it more suitable for ML. R is quite useful for statistical analysis, and so many organizations use both languages.

Note: Python supports R functionality with the RPy2 package.