Have you ever asked yourself, 'What is Python language, or what is Python used for?' Well, Python is the preferred language for Machine Learning. And unless you have been living under a rock over the past decade, you should know by now that Machine Learning is presently the hottest trend in today’s tech community.
Many years ago, no one would probably have imagined a world with self-driving vehicles and smartphones that inform you out loud what the weather will be like the following day, except perhaps in the movies.
Software apps now learn by themselves without a human's necessary intervention, all thanks to what is known as Machine Learning.
According to Forbes, Machine Learning patents grew significantly at a 34 percent rate from 2013 to 2017. And there is no doubt that it is going to skyrocket in the future.
Python remains the primary or preferred programming language used for most of the research as well as development in Machine Learning, according to GitHub.
And although it is somewhat clear that Python is the most popular programming language for Machine Learning, this article explores why this is so. What is Python Language, and what is Python used for? These questions will be answered as you read the following paragraphs.
What is Python Language?
If you want to know the answer to the question “What is Python language”, you should first know “what is Python?”.
It has modules, exceptions, classes as well as dynamic typing and high-level dynamic data types.
So, what is Python? Python is a general-purpose coding language – an object-oriented, interpreted, and interactive high-level programming language with built-in, dynamic semantics.
Python is used as an extension language for apps written in other languages that need automation interfaces or user-friendly scripting.
To answer the question, ‘what is Python used for?’ this in-demand programming language can be used for the following:
- Data science
- Performing mathematical computations and process big data
- Back end (or server-side) mobile and web app development
- Software and desktop app development
- Writing system scripts, etc.
Newbie coders tend to get scared of Python due to its broad range and multiple applications or uses. However, there is no need to be because Python – just like many other familiar-sounding programming languages – is easy to learn.
This programming language is widely in demand. And learning how it works and how to use it for various applications will significantly boost your chances of getting hired by the top guns in the tech world and making mouth-watering income within a few months.
Python's ease of use and efficiency means a streamlined QA, less development time as well as the debugging process. It also offers a much greater return on investment when compared to those hard-to-navigate coding options or alternatives.
Startup companies love Python since they usually need to run leanly in order to survive, especially when they build digital products – e.g., mobile apps, websites, or software programs.
Using Python programming language for Machine Learning is pretty cool; in fact, this programming language is the most preferred for Machine Learning. The latter includes things like financial services, speech recognition, as well as most of the recommendations that the #1 movie streaming service provider, Netflix, serves up from time to time whenever you log in.
With this information so far you have gained more insight to help you answer the question “what is python?”
Python’s Increasing Popularity
Python is arguably becoming one of the most popular programming languages in the world today, thanks to its versatility, simplicity, and ease of maintenance. It is also the programming language of choice for big brands, including Google, Facebook, Amazon, Quora, and Netflix.
The programming language is frequently used in some of the most innovative and exciting technologies, including Machine Learning, Robotics, and Artificial Intelligence.
Besides, Python is presently an in-demand programming language in universities – as the most popular introductory language – and the tech world. More developers – from beginners to professionals – are learning Python and its multiple applications in order to build on their skill portfolio.
Developers know the answer to “what is Python used for?”
The more people and firms use Python; the more resources are created or developed around it. The programming language is also the go-to option for developers working on complex tasks without experiencing any coding problems.
Now that we've gotten this out of the way by providing answers to questions such as 'What is Python language?' and 'What is Python used for?'
Let's find out why Python is the preferred language or best suited for Machine Learning.
The only downside that Python seems to have – which is negligible – is that the programming language is somewhat slower than other third-party languages. However, the data handling capacity of the Python programming language is exceptional.
Reasons why Python is the Preferred Language for Machine Language
Python Programming Language is User-friendly with a Low Entry Barrier
Believe it or not, there is a universal deficit of programmers. No one likes complicated things like coding and other sophisticated jargon that many programming languages are associated with.
The user-friendliness of Python is perhaps the #1 reason why it is most preferred by experimental beginners and veteran developers alike for Machine Learning. This makes the entry barrier extremely low.
The simplicity of this in-demand programming language means that developers at all levels can focus on solving Machine Learning problems instead of spending energy and time understanding every technical nuance of the language.
It also means that the more data scientists can master Python, the faster they can readily get involved in Machine Learning projects.
For instance, a Machine Learning engineer's job is to extract, process, refine, clean, arrange, and make sense of data to develop intelligent algorithms. The concept of linear calculus or algebra can be incredibly complex, thereby taking the maximum effort.
But Python can be quickly implemented, and this helps Machine Learning engineers to effortlessly and promptly validate ideas without stress. This gives more in-depth information on the question “What is Python?”
Moreover, Python is superbly efficient. This programming language allows you to complete a lot of work using fewer code lines than other alternatives. Python codes are incredibly understandable by humans.
It is remarkably similar to the English language, making it easier to learn, thanks to its simple phrase structure that allows developers to work confidently with complex systems.
Python is Easy to Read
If you understand the answer to the question “What is Python?”, then you know that it is incredibly easy to read the Python programming language; this means that any Python developer can quickly implement the change, share or copy it whenever there is a need to change the code.
Using the Python programming language, therefore, eliminates mistakes, confusion as well as conflicting paradigms. These, in turn, boost the efficiency of sharing of ideas, algorithm exchange, and tools between Machine Learning and Artificial Intelligence specialists.
Furthermore, tools such as IPython offer additional features, including debugging, testing, tab-completion, etc. It allows for execution, parallel application development, and interactive monitoring.
Python Comes Embedded with Multiple Frameworks and Libraries
Python is one of the fewest programming languages that come with hundreds of different frameworks and libraries that developers can avail themselves of. A library is a collection of routines and functions that a programming language can utilize at any time.
Developers can use these wide varieties of frameworks and libraries efficiently, thereby saving a lot of time. Having access to these libraries and frameworks also means that developers can carry out complex tasks without necessarily rewriting many lines of code.
And since Machine Learning relies heavily on statistics, mathematical optimization, and probability, Python libraries help data scientists execute various studies seamlessly and quickly. These benefits have pushed this programming language up the ranks, making it much more popular than its counterparts!
Several Python libraries are used explicitly for both Machine Learning and Artificial Intelligence. Here are some of them:
- Scikit-learn: This is a free software library for Machine Learning. Numerous regression, clustering, classification algorithms relate to this library. Scikit-learn can also be used in combination with SciPy and NumPy.
- TensorFlow: TensorFlow is also a free software library employed for various Machine Learning applications such as neural networks, etc.
- Keras: Keras is an open-source library that focuses on experimentation with deep neural networks.
- Pandas: It is used for high-level, general-purpose analysis and data structures.
- StatsModels: StatsModels is employed for data exploration and statistical algorithms, etc.
- NumPy: This is used for data analysis and high-performance scientific computing.
- Matplotlib: This is used for histograms, 2D plots, charts, etc.
- Seaborn: It is used for data visualization.
- SciPy: SciPy is used for advanced computing.
These are just some of the libraries that make Python the most preferred programming language for Machine Learning.
Python Programming Language is Versatile
A vital part of software development is testing. Python for Machine Learning can run practically on any platform, including Windows, Linux, macOS, Unix, and so much more. This makes testing a walk in the park since developers can carry out tests on any platform.
All that is required is for developers to use PyInstaller, for instance, to set their code to run on numerous platforms. This means only one thing: utilizing Python for Machine Learning will save you a lot of money, time, and effort!
Python is Extensible and Portable
The fact that Python is extensible and portable is another reason why the programming language is prevalent in Machine Learning. Several cross-language operations can be executed quickly on Python as a result of these characteristics.
Many data scientists love using GPUs (Graphics Processing Units) for training their Machine Learning models on their own machines, making the extensible and portable nature of Python highly suitable for this.
Python is Flexible
Python for Machine Learning is well-loved because of its great flexibility. Developers can make use of Python along with other programming languages in order to achieve their goals.
There is no need to recompile source codes, meaning any changes can be made and implemented instantly, meaning that you can view results pretty fast or in real-time. The flexibility of Python also makes the probability of bugs occurring very minimal.
Python has Corporate and Community Support
Do you know that Python has been around as far back as 1990? That is more than enough time to create a highly supportive and strong or vibrant community. Since Python is an open-source programming language, it implies that you will have access to a vast and generous pool of resources that you can utilize, whether you are a beginner or even a pro.
And as a result, Python learners or newbies can quickly improve their Machine Learning capacity and knowledge, leading to the increasing popularity of the programming language.
If you experience any challenge in your journey to becoming a skilled Python developer, someone in the community will offer you a helping hand.
Several frequently-occurring issues are discussed in Python communities and forums. There are also several resources or Python documentation that are available online to promote Machine Learning in Python!
Of course, corporate support is a vital part of Python for Machine Learning’s success in the tech world. As mentioned earlier, Google, Facebook, Netflix, Quora, Instagram, etc. use this innovative programming language for their extensive products.
On record, Google is single-handedly responsible for developing most of the extensive Python libraries for Machine Learning such as TensorFlow, Keras, etc.
Python can be seamlessly integrated with .NET components or C/C++ libraries and Java because of its extensible nature.
Python is undoubtedly the best and preferred choice/programming language for Machine Learning. It is user-friendly and helps to make data validation practically error-free and quick.
When developers have access to a widely developed library ecosystem, they can carry out tasks that do not require extensive coding.
Testing can also be conducted efficiently and on any platform, including Windows, Linux, Macintosh, Solaris, and Unix, thanks to Python’s versatility.
As a result, newbies and pros alike have picked up Python, making it very popular and easier for firms to recruit developers that possess the right competencies to get deeply involved in Machine Learning projects.
With these points and information, you should have the answer to the question “What is Python language” or “What is Python used for?”