Is Python Enough for Machine Learning?

Python is becoming more and more popular every day, especially in the field of machine learning. 57% of machine learning engineers and data scientists prefer using it for development. But is Python all it takes to become a machine learning engineer, or do you need to learn technologies other than Python as well?

Python is more than enough as a programming language if you want to get into machine learning. However, you’ll need to learn several other skills such as ML algorithms, database management languages, mathematics, and statistics in order to become a full-fledged machine learning engineer.

Why do data scientists and ML engineers prefer Python over other programming languages? Read on to learn more about Python’s popularity and its alternatives.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Is It Enough to Learn Python?

The question may seem simple, but it is not. You see, Python is just a tool; it is what you do with it that matters. Machine learning is not about learning a particular programming language; it’s about building intelligent software and systems for machine learning. If you are to become a real ML engineer, you will have to learn everything the position asks for.

Python may be enough for data science as a programming language, but that does not mean you have to learn only Python. You also need to know other things like SQL, Python libraries, mathematics, and statistics to become a practical ML engineer.

As we’ve said, Python is just a tool, like a screwdriver. If we asked you whether a screwdriver is enough for a mechanical engineer, you would laugh at me. The same goes for machine learning. Other than the programming language, algorithms are a huge part of ML, and without them, you will get nowhere.

With that said, Python is the most popular programming language for machine learning. It is the primary choice of most ML engineers. It has a massive collection of libraries and frameworks to help you build models effortlessly.

If you are just starting out with ML, we suggest you stick to Python until you become an expert at it. Rather than dabbling with several technologies at once, it would be better to learn and master Python for ML first. You can then expand your skillset by learning R and perhaps other programming languages or technologies.

To learn more about how you can learn machine learning without boot camps or expensive classes, read this article: How to Teach Yourself Machine Learning in 5 Steps. It’ll teach you the essentials of ML and help you land a job in the field.

Why Python Is the Preferred Language for Machine Learning?

When we compare programming languages used for machine learning, Python ranks number one. Its readability and simplicity allow beginners to focus on learning ML algorithms rather than waste time dabbling with the programming language. 

It is even used by tech giants like Google and Facebook, which means it’s a reliable language and suitable for performing complex development tasks as well.

What else does Python have that most languages don’t? Why is it the go-to language for machine learning? Let’s look at the top four reasons why developers prefer Python for ML purposes.

Simple

Machine learning involves working with large amounts of data. Since Python is easier to learn than many other programming languages, most data scientists go with it. It allows them to focus on solving ML problems rather than spend time learning the nuances of the language.

Python is a simple language with concise and readable code. An English-speaking person can comprehend the code, even with no prior programming knowledge. Since it’s more intuitive than other languages, it’s easy to build models in Python for ML, and it’s better to have a simple programming language in a complex field such as ML.

Flexible

With Python, you can choose to use either Object Oriented Programming or scripting. It allows you to quickly see the changes you’ve made without recompiling the source code every time. You can even combine it with other programming languages to achieve the desired result.

Programmers can choose their preferred programming styles, thanks to the flexibility of Python. They can code in an imperative, functional, procedural, or object-oriented style. It decreases errors as developers can work in whichever way they feel comfortable and solve problems in the simplest way possible.

Lots of Frameworks and Libraries

A library is a set of pre-written code that helps you perform a specific task. Libraries reduce development time and help programmers build products faster. Python has more libraries than any other programming language for ML and AI. For every possible issue in machine learning, there’s a Python library. 

Here are some of the popular ones used in the ML field:

• Scikit-learn, TensorFlow, and PyTorch are used for machine learning purposes.
• Matplotlib and Seaborn can help you visualize data.
• SciPy and NumPy allow you to perform scientific and advanced computation.
• Pandas is designed for general-purpose data manipulation and analysis.

Large User Community

In the development survey 2020 by Stack Overflow, Python was the fourth most popular language in the world. It came into existence in 1991, which means it’s a mature language. In these 29 years, a strong and helpful community has grown around it.

You can find hundreds of online courses, YouTube videos, and forums that’ll guide you through problems or help you learn new topics easily. When you google a problem you’re facing, odds are, somebody has faced the same issue and already posted a question about it.

Several tech giants like Google, Facebook, Quora, Netflix, etc., use Python for developing their products. In fact, many Python libraries, like TensorFlow, Keras, etc., have been built by Google.

What Languages Can You Use Instead of Python?

We’ve seen why Python is perfect for machine learning, and it’s also the most popular programming language for ML purposes. However, there are other languages, as well.

You can use any of the following for machine learning. All of them have their pros and cons; they are better than Python in some aspects, while worse in others. So, if you don’t like Python for some reason or just don’t want to use it, here are your options:

• R: After Python, R is the most commonly used language for ML. It has its roots in data analysis, data visualization, and statistics. Compared to Python, R is often reported to be slower and laggier when dealing with large-scale data products.
• Java: It is excellent for search algorithms, natural language processing, and neural networks. It is transparent, portable, and supported by many libraries. However, Java is not the right language for statistical visualization and modeling; Python has much better tools for it.
• Scala: It combines functional and object-oriented programming, and it is excellent for real-time data analysis and big data. Compared to Python, it has fewer ML tools. Still, Scala is highly maintainable and a decent choice.

Conclusion

Learning Python is an excellent step toward becoming an ML engineer, but it’s not the only thing you need to know. 

Algorithms, mathematics, statistics, and database languages are also just as crucial, but as far as programming languages for machine learning are concerned, Python is the most popular. It’s not only readable and straightforward but also reliable and flexible. Its many libraries help ML engineers perform tasks quickly and efficiently, while the large user community provides support and tutorials.

R code is a well-known alternative to Python. Although there are others like Java and Scala, they aren’t as widely used.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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