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Why Machine Learning Has Become so Popular?

ByDavid Adamson

Why Machine Learning Has Become so Popular?

Over the last few years, the machine learning and deep learning industry have been booming. There are plenty of new start-ups starting every year and many machine learning companies have also become multi-million companies. There are many reasons behind the popularity of Machine learning.

Here, in this article, we have shared different reasons, why machine learning has become so popular over the last few years.

If you are studying computer science and looking for a full-time job in this field, then learning Machine learning concepts can be very helpful.

Machine learning has become increasingly popular in recent years for a number of reasons, including:

  1. Huge amounts of data: With the exponential growth of data generated by individuals and organizations, machine learning has become a critical tool for processing and analyzing this data. Machine learning algorithms can help identify patterns and relationships in large amounts of data, making it possible to extract valuable insights and predictions.
  2. Improved computational power: Advances in computational power and the availability of powerful GPUs have made it possible to train machine learning algorithms on large amounts of data, which is essential for achieving accurate results.
  3. Real-world applications: Machine learning is being applied in a growing number of real-world applications, including image and speech recognition, recommendation systems, and fraud detection. This has helped demonstrate the practical value of machine learning and drive its adoption.
  4. Open-source tools: The availability of open-source machine learning libraries and frameworks, such as TensorFlow and PyTorch, has made it easier for individuals and organizations to get started with machine learning, reducing the barriers to entry and enabling wider adoption.
  5. Investment in AI: Increasing investment in AI by large technology companies, startups, and governments has driven the development of machine learning and related technologies, and has helped to promote its wider adoption.

Overall, the combination of large amounts of data, improved computational power, real-world applications, open-source tools, and investment in AI has helped to make machine learning one of the most rapidly growing and popular areas of technology today.

Innovation makes it interesting

Being innovative is the biggest advantage of machine learning concepts. You can develop something that no one has seen before and attract a larger audience to use your product. For example, with the help of machine learning, you can develop an app that recognizes all the objects and shows you the correct count. In this app, a user needs to just upload a picture of the object and AI/ML machine counts the total number of objects in that picture and returns the count.

This app has been trending and this website has got thousands of views over the last month or two. Apart from that, Machine learning concepts innovation can also help in other industries like education, pharma, accounts, public relation, and many others.

Flexibility in binary programming programming

The concept of binary to decimal conversion plays a very important role in the development of any machine learning product. Now, with the innovative frameworks, it has become very flexible to perform any kind of binary to decimal programming. All you need to do is just install a node.js plug-in to convert binary to decimal and then with just one line of code, you can easily develop and implement a binary to the decimal converter in your project. For example, you can consider this website to learn.

A huge amount of data

One main thing a deep learning machine requires is nothing but a huge amount of data. This data entry needs a value of a minimum of 1 million entries. For a normal human being, it is next to impossible to gather 1 million data and then train the model. To resolve this issue, there are many web scraping tools available that developers can use to gather the data that is available online and then use that data in their app. With the help of these libraries, developers can gather data in just a few minutes and start training Machine learning models quickly. There is a Python library called scrappy that is useful for web scraping easily.

Ready-made frameworks and library

Ready-made frameworks and libraries attract more developers is the biggest reason for the machine learning products. Most developers use the Python programming language for deep learning. Python is an open-source programming language that lets the user use any package totally for free. Along with that, users can also modify the package and upload their package as well. This attracts more developers to contribute and develop more packages. This is the biggest reason behind the expansion of machine learning projects. Not only that but there are also big players like Google and IBM involved in the development of deep learning products.

A huge community behind it

As discussed above, there are big players like Google and IBM involved in the deep learning projects. You can get solutions for your every query in the form of packages. If you do not find a correct answer after implementing packages then you can also ask on the community forum. Millions of users use this kind of forum and they can help you to find the correct solution for your product. Along with the Python community, there are many other communities like Stackoverflow and StackExchange that can also be useful.

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So, these are the best reasons why machine learning has become so popular among users. If you know any other reason why machine learning has become so much popular then do share it with us. Your contribution will help other users to gain more knowledge.

About the author

David Adamson administrator

David Adamson is the founder and digital strategy manager at Coin Ideology Digital. He develops techniques to boost traffic, sales, and brand awareness for startup agencies. He has specialization in Blockchain and digital marketing industry including SEO, PPC, SMO, influence marketing and consumer behavior analysis.

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