Why does Python suit Machine Learning?

Skills in Machine Learning are in great demand these days. With the amount of data around us increases exponentially day-to-day, taking decisions by dissecting the data was the way forward for a lot of companies. Working on Machine Learning algorithms involved complex problems in itself (managing data and mathematical solutions, etc.). The computer language to work on should be hassle-free for these engineers. That is where Python comes into the picture.

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What is Python

Python is a high-level programming language that was started off in 1991 by Guido Van Rossum as a side project. Now, it is one of the most, if not the most, sought after language by the developers across the globe. Software companies all around the world would want their employees to be proficient in Python and for good reasons. The language is being loved by the coding community for its simplicity and no-hassle structure.

To understand the reasons for such demand towards this software language, the ethos of Python as per Software Engineer Tim Peters – “Zen of Python”, serves as a good indication. Simplicity, Readability and Community are a perfect three word summary for Python.

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Advantages of Python

Simple to code

Python predominantly uses simple English words as commands. This makes coding extremely easy to write and interpret. You can just say – print(“Hello, World!”) on Python. Nothing else, not even a semicolon.

Easy to debug and understand others code

Any moderately experienced developer would know these two things about coding – Debugging and need for understanding someone else’s code. While you’ll not get your code right in the first iteration (almost, no one ever would be), debugging is the way to anything fruitful in a coder’s life. Easier the problem to find, happier would be the coder.

Code changes hands a lot of times. Interpreting is not going to be straightforward as each of them would have their own coding styles and naming conventions, etc. Python generalises a lot of the commands thereby neutralising the stylistic factor as a hurdle.

A lot of them use it for a lot of purposes

Python is open source. So, there’s a huge, passionate community consistently working on the language. And, people in the community help each other by making most of the solutions available for everyone to use on platforms like numpy, etc.

What is Machine Learning?

Simply put, Machine Learning is an exercise where you feed information to the computer and ask it to make accurate decisions and fetch relevant insights. For example, you collect data about mails tagged with spams and feed it to the computer. The computer, if skilled in Machine Learning, would identify the spam mail the next time a new mail comes through.

Another example, you feed the ML-driven computer with the data of tumor types and the ages. The next time you input a tumor type, it would predict the potential age of the patient.

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Challenges of a Machine Learning Engineer

Of the many challenges that an ML engineer would face, two of them stand out.

Data

There’s a lot of data out there in the world. But it is ugly to look at. It is unstructured, most probably incomplete, predominantly large in size and raw in nature. So, quite an amount of time of the ML engineer would go into sorting out the data in hand. He/she would need to extract, clean, arrange and process the data. Experienced experts say it takes nearly 60-70% of the total time in sorting the data. So, it’s long, tiresome and hard. Sounds cool, but engineers should develop the machine learning algorithms to solve for these problems (the above mentioned and many others). They have to use a software language to develop these algorithms.

Complex

Once the data is sorted (which in itself is a huge task, as explained above), identifying the ML solution is essentially an exercise of finding the unknown solution. To find the patterns in the data, it requires the ML engineers to apply complex mathematical functions and techniques. But, doing it once isn’t enough. They’ll have to keep trying variations of these mathematical applications the multiple numbers of times until the desired outcome is achieved. So, the implementation of these complex functions and equations would have a direct affect on the time and energy spent in finding the solution.

Why is Python ideal for Machine Learning?

There are quite a few authentic reasons as to why people are ramping up their Python skills for Machine Learning.

Easy to Learn

Learning Python is easy. There is a very small learning curve involved in implementing the basics of the language. When compared against c, c++ or java, python can be learned and coded in a surprisingly smaller amount of effort and time. Python has a very robust object-oriented structure to it. It is easy to interpret as a language and extremely interactive making the debugging slightly simpler than the nuisance it generally is. The syntax is smooth and clear. When coupled with its solid engine, it is a great language to implement programs. So, the ML engineers can focus on solving for the algorithms rather than work on knowing the syn taxes of the language.

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Open Source

Open Source means something is free to use, to put it in layman terms. This makes a lot of people use it. Python is open source. To give an example that is not, Matlab is not open source. You need licenses to use Matlab. That essentially limits the usage of the product.It is a superpower to have a lot of people use something. For python, there are a lot of people working on a lot of problems, day in day out, on the language.

They get together and share the problem statements along with the solutions, thereby making the lives of the other computer programmers easier. The greater the number of people who use it, the higher are the number of problems solved across the world and thereby the community grows stronger making it easier to work on the language. It serves the ML community well to share knowledge. This becomes all the more essential given the severity and the difficulty of the problems solved on ML.

Packages

These communities that are mentioned get together and create solutions for general problems and circulated in the form of packages. Imagine packages as this – straight forward solutions for problems that are hard and not necessary to solve for every time. And what’s better, there’s almost zero learning curve in implementing these packages. Meaning, these solutions can be used directly on the problems without actually knowing what’s going on within.

An example – You are working on data types that involve arrays and vectors. You would not like to write the array multiplication or vector translation every time you use these data structures. Numpy packages that are available online as open source (mostly) to cater to these functionalities. All you need to do is download the package and go ahead and use the functions that are necessary to solve your problem. ML engineers are greatly benefited through these packages in solving the redundant challenges they face while solving for a certain problem and concentrate on what’s important.

To Summarise

Machine Learning is the exercise of Computers trying to find patterns and take decisions by going through huge amounts of data. And, it takes the developers to come up with effective algorithms to do so. As the process of arriving there is cumbersome due to unstructured data and complex mathematical applications, working on an easily understandable and interpretable coding language turns essential.

Python fits the bill perfectly for ML developers as it is pretty straight forward to work with. Clubbed with the ease of learning, being free and open for everyone to use and the strong community that helps each other, the ML developers can focus their energies primarily on things that need their complete attention rather than the language they have to code.