Why is Python used for machine learning?

When we see demonstrations, videos, and tutorials about machine learning, most likely the examples will be in Python.

Why is that?

What is it about the programming language Python, which makes it so popular? That is what we will be discussing in this article. But first, to give you the short answer to the question:

Why is Python used for machine learning? Python is most often used for Machine Learning for the following reasons:

• Easy to understand. This means it is suitable for data scientists and not just seasoned developers.
• Tons of external libraries for different applications like Deep Learning, image processing, data visualization and much more.

What is Python?

Before we get to the other things, let us tell you about what Python is. In a nutshell, Python is one of the most popular programming languages in the world, and it is preferred by beginners due to its ease of learning and by professionals due to its scalability.

The big plus for Python is every growing popularity and tons of libraries which make it an ideal choice for any project ranging from simple Web Apps to Machine Learning Projects.

What is Machine Learning?

Now if you are someone who knows what Machine Learning is, then you can skip this part but if you are someone who doesn’t know what Machine learning is, then pay attention here.

Machine Learning is basically the application of Artificial Intelligence, in this field, we study algorithms and statistical models of computers that use them to improve their performance. In layman terms, in this field, we use the data to make our machine intelligent and it will allow it to take the intelligent decisions.

For example, if you want to build a spam filter for your email service, you will train your computer to learn the rules of spam detection by looking at the previous data or based on the keyword. So basically, in Machine learning, we make the machine learn the patterns in different datasets.

How is Python different from other programming languages?

In this section, we will tell you how python is different from other languages by comparing it with other programming languages. Let’s do it.

Python vs PHP

Python is a versatile programming language which can also be used for web development, where PHP is a web-oriented programming language. If we compare these two languages then we can compare them in the following ways.

• When it comes to popularity both languages are very popular among programmers, but there is a catch. PHP is still a very popular programming language but it is declining, there is nothing new added to the language in the last few years, where python is evolving every year. The popularity graph of python is also on the rise.
• PHP has just a few frameworks where Laravel, Codeigniter, Symfony are the popular ones. Python has tons of frameworks available from the community.
• Python is also very easy to learn for beginners as it allows them to make the mistakes in their code without breakages and it provides confidence to the beginners. Where PHP has difficult syntax and you will need more time and efforts to master the language.

Python vs C#

C# is another popular programming language and it is also used in the industry by many companies. Python is different from C# in many ways but both languages can be used for web development. Following is a quick comparison between both languages

• When it comes to simplicity, there is very little competition for Python. Python was designed with the aim to provide the programming language just like English and make it easy for beginners. Where C# has complex syntax and it also follows the difficult coding concepts which require both time and effort to learn.
• Another big plus for Python is its ever-growing libraries which we will discuss later in this guide. Where C# has only a few libraries which are a disadvantage for the C#.
• When it comes to performance we would like to highlight that C# has more features than Python, Its performance is also much higher than Python.

Python vs Ruby

Both programming languages are object-oriented programming languages and are very popular in the industry. Following are some of the key differences between them.

• When it comes to the community, both programming languages enjoy immense support from the industry and professionals around the world. The Python supporter group is one of the largest supporter group in the world which not only include thousands of professional developers, but also companies like Google, Yandex, Dropbox, Mozilla, and Microsoft.
• When it comes to Syntax, Python has very simple Syntax which is very easy to learn. On the other hand, Ruby has a very complex syntax which requires both time and effort to master.
• The big plus for ruby is that it offers multiple solutions for the same problem where Python usually offer a single solution to the problem.

Why Python?

Now many of you will ponder what makes Python so ideal for projects involving AI, Machine Learning and Deep learning. Lets’ take a look at the factors which make it so ideal for AI projects.

Variety of libraries and Frameworks

One of the major reasons why Python is used for AI projects is its abundance of libraries and projects.

Python community is one of the largest programming communities in the world and its ever-growing community regularly add new libraries and frameworks to it.

Libraries and frameworks help the programmers and you can save a lot of time by opting for the specific library for your project.

When we take a look at different libraries we notice why it is ideal for AI projects. Let’s take a look

• If your project involves any image work then you can use libraries like Numpy, OpenCV, and Scikit which have a ton of pre-programmed functions which you can use with few lines of python code.
• If your work involves processing the Text, then you can opt for Nltk, Numpy, and Scikit.
• If you are working on a project which involves some audio processing then you can use the package LibROSA and save a lot of your time and effort.
• If you want to solve any machine learning problem then Pandas and Scikit are available at your disposal.
• If you want to process the data in your project then Matplotlib, Seaborn, and Scikit are good options for you.
• If your work involves some deep learning process then Tensorflow and Pytorch are the clear choices for you.
• If your work involves some scientific computing process then Scipy is a good option.
• If you want to integrate web applications then you can use Django.

The simplicity

Python is one of the simplest programming languages in the world and it was designed in a way that it should be concise, easy to read and understand. Its simple syntax makes it ideal for projects involving AI, Machine Learning and Deep Learning.

Let us explain, AI projects use the extremely complex algorithms and workflow and if we couple it with complex programming language it makes it difficult for the developers to focus on finding a solution for the problems.

Python has a simple syntax which makes it ideal for developers to write code and test the complex algorithms. In addition to that, its readable code makes it easy for other programmers to understand it.

Community

Python has one of the largest community in the world, as it is open source language it is supported by thousands of professional developers around the world. You can always reach the community on many python forums where you can get help from the experts.

Is it possible to do machine learning in other languages?

The simple answer to this question is that yes it is possible to do machine learning with other programming language and it is also true that there are many languages that offer much more functionalities than Python. In this section, we will look into some other languages which you can use for machine learning.

R Programming Language

R is a programming language which was developed as a modern version of another programming language known as “S”.

R programming language implements the S language combined with lexical scoping semantics which makes it more flexible in producing statistical models and this is the very reason why it is one of the best languages for machine learning.

You can use R to develop powerful algorithms and use R Studio to have statistical visualization of your algorithms. R programming language is also a very popular language for academic research.

C Programming Language

The C programming language is considered as the mother of all programming language and it is also most-suitable for predictive algorithms. The C programming language is for professionals and if you want to use it in machine learning then you should have a very strong fundamental of computer science.

However, once you pass the learning curve and master C programming language, no one will be able to stop you from developing advanced algorithms for machine learning.

Java

Java is another great programming language and you can use it for AI development projects. Java has complex syntax and you will need to dedicate time and efforts to learn it, but once you are done with the learning process, you can develop advanced algorithms with Java.

Lisp

Lisp is one of the oldest yet most suited programming languages for Machine Learning. It was invented in 1958 by John McCarthy who is considered the father of Artificial intelligence.

Lisp programming language is known for its prototyping capabilities and creation of new objects without automatic garbage collection. Overall Lisp is still one of the best programming languages for Machine learning but over the years it has lost its uniqueness as you will find these features in modern programming languages with some of their own unique benefits.

Best Resources to learn Machine Learning in Python

Now let us share some resources for you which you can use to learn Machine Learning in Python. Let’s take a look.

• If you are new to the Machine learning and want to learn it in detail, you can use this resource as in this course you will learn the basic concepts of Data structures. If you want to learn Machine learning then you must also learn how the machines perceive the data and this tutorial can help you to learn it. (https://www.geeksforgeeks.org/data-structures/)
• Bootcamp is always a great resource for learning things and “Python For Data Science and Machine Learning Bootcamp” is great for those who want to learn the Machine Learning and in this course, you will learn how to use the different libraries and technologies for Machine Learning.
• If you are into Books then you can read Introduction to Machine Learning with Python by Andreas C. Mueller and Sarah Guido. In this book, you will learn the fundamentals of Machine learning with Python and how to use the Scikit for your projects. If you are beginner then this is a good resource for you.
• If you are into video tutorials then you can use Python Course for Beginners by FreeCodeCamp. In this 4 hours long video, you will learn the fundamentals of Python in great depth and once you are done with the fundamentals you can move up to the more complex methods. If you are an absolute beginner, then we would recommend this course and if you are comfortable with Python then you can take Python Bootcamp.

Conclusion

Artificial Intelligence is the hot topic these days as companies are working on their products to make them smarter and Machine learning can play an important role in it. When it comes to Machine Learning with Python, many developers choose Python over any other programming language because it is simple, which make it easy for fellow team members to understand the code.

It also has tons of libraries which can save developers from writing a lot of code. We have shared the other programming languages which you can also use for machine learning, but we think that Python is at the cutting edge and you will see significant progress in this language over the course of next few years.