One of the strongest cases that anyone can make for picking up Python as the next language to learn, would be the growth that it has seen in the past 3 decades since its inception. What started out as a small project was embraced lovingly by the developer community and this has made it one of the top languages in the programming world today.
The testament to its growth is the variety of uses that it can cater to. The strength of survival for any programming language today is if it can stay in step with the changing needs of the modern world. Automation, artificial intelligence and machine learning are the buzzwords today, which are effectively going to power the products of tomorrow. If you’re looking to keep yourself relevant, then a combination of Machine learning with Python trainingshould be your go-to approach in such a scenario.
There are some major reasons why Python has become a part and parcel of anything within the domain of machine learning today.
Changing view from Java to Python
But when it comes to deeper and complex tasks that involve the core of machine learning, the perspective needs to shift from what seems good for the business, to what seems right for the task. Python is loved more by the programming community than the companies purely for this reason. Machine learning and automation depends heavily on understanding how the code functions, and that requires an in-depth view of the granular details. Today’s employers have begun to understand that and have started looking for Python experts for their needs, too.
Excellence in pattern recognition
Machine learning can come in all shapes and forms. It could be as simple as detecting a spam mail coming to your mailbox, or as complex as navigating a driverless car in the rush hour traffic. While the complexities of these tasks are extremely different, what ties them both together is the principle on which they function. And that is pattern recognition.
Nearly all machine learning algorithms are dependent on helping it understand the data that it brings in. Furthermore, having multiple data points that provide for endless data streams is not useful unless we can make sense of the data. Python helps transform data into actionable information which can be used for machine learning. It is able to identify the key data points, work towards structuring it in a way that can be worked upon, pick up patterns that repeat at varying degrees, and help build the logic for the machine to deliver upon the functionality it is being created for.
Availability of various data packages
As you begin to understand the applications of machine learning, it becomes clear to any person that they are as wide and vast as they can be. The diversity of what machine learning can do is heavily dependent on the variety of data sources that exist out there. Every data source has its own format and its own raw information that it carries with it. Inputs can be available in the form of digital signals, images, audio, video, application responses, data visualization, text documents, scanned files, and so on.
To be able to process every kind of data with its unique formats and structures would require a programming language to be extensively powerful. There do exist a few such languages but their learning curves and processing powers are so large that it seems impractical to make use of them. Which is where the democratic abilities of Python swoop in and save the day. The general-purpose application of Python can be enhanced to handle specific problems by use of data packages. These are available for use free of cost, thanks to GNU licensing, and exist to serve every possible data form out there. Import the package and you’re good to go!
Simple and quick prototyping power
This is one of the major needs for machine learning and one of the biggest strengths that Python provides. Implementation and testing form an important part of machine learning since it helps you test your code and check if the functionality works as expected. Most of the times this goes hand-in-hand since code development changes in real-time as a result of it.
Python helps you build prototypes rapidly and check for outputs almost in parallel to your coding. Using modules, codes can be replicated and optimized with ease, making it extremely simple and fast to reach the final destination. Taking care of Machine learning with Python interview questions can be a breeze once you get acquainted with the language and its applications. Pretty soon, you’d be a Python expert and your resume would be the one that head hunters would want.