{"id":69224,"date":"2019-09-09T17:38:34","date_gmt":"2019-09-09T12:08:34","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=69224"},"modified":"2025-07-27T12:17:51","modified_gmt":"2025-07-27T06:47:51","slug":"why-machine-learning-is-popular","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/why-machine-learning-is-popular\/","title":{"rendered":"Why is Machine Learning so popular? – From a techno geek’s diary"},"content":{"rendered":"

What is Machine learning? How can Machines learn? Machines don\u2019t have brains to learn! Or do they? <\/strong><\/span><\/p>\n

Do your mind also boggles from curiosity when you hear such terms? It\u2019s okay! After reading this article – why machine learning is popular, you can also use such jargon in front of your friends!<\/p>\n

Machine learning is an application of artificial intelligence (AI)<\/strong>. The system provided by ML has the ability to automatically learn<\/strong> and improve<\/strong> from past experiences. So, they can perform without being explicitly programmed<\/strong>. It focuses on the development of computer programs which can access data<\/strong> and use it to learn for themselves.<\/p>\n

In simple terms, this field of computer science provides computer the ability<\/strong> to learn without being explicitly programmed. It provides algorithms which can be trained to perform a task.<\/p>\n

Reasons why machine learning is popular<\/h3>\n

 <\/p>\n

    \n
  • The modern challenges are \u201chigh-dimensional\u201d<\/strong> in nature.<\/li>\n
  • With rich data sources, it is important to build models that solve problems in high-dimensional space<\/strong>.<\/li>\n
  • Through it, the models can be integrated into working software<\/strong>. It supports the kinds of products that are being demanded by the industry.<\/li>\n<\/ul>\n

    Also, Google Trends<\/strong> that tracks the popularity of search terms<\/strong>, suggests that searches for machine learning are about to out-pace the searches for artificial intelligence. Machine learning is moving beyond textbooks and is creating<\/strong> a disruption<\/strong> that will revolutionize<\/strong> the future<\/strong>.<\/p>\n

    Now, let’s learn in detail – why machine learning is gaining popularity –<\/p>\n

    1. To sort prolific and unstructured data<\/h4>\n

    A lot of information is available today because of IoT<\/strong>. It is not possible to manage every information<\/strong> or data<\/strong> coming from email<\/strong>, social networking<\/strong>, blogs<\/strong>, podcasts<\/strong> or any other source for that matter.<\/p>\n

    Also, to keep that information in a structured manner it is also necessary to keep up with the trend and gain a competitive edge<\/strong>.<\/p>\n

    If blunders like missing useful content occurs then a business might lose a fortune. No one knows where the idea can come from and strike you.<\/p>\n

    For e.g.: Jennifer Lopez’s Grammys award green dress inspired Google<\/strong> to come up with the image search feature.<\/p>\n

    For marketers, the stress of finding and tracking the best content is very real. But, Machine Learning methods are a savior for them. It helps them to provide the tools to locate and recommend the most relevant content in order to overcome information overload.<\/p>\n

    Another crucial factor that has boosted the use of machine learning is that it can support and enable prompt decision-making. Ever \u2013 growing dependability on fresh data from such business areas as finance, healthcare, and e-commerce is another facet where machine learning algorithms assist in making prompt and precise decisions based on relevant data. Such applications as real-time fraud detection, target consumer marketing, and automated trading require real-time processing abilities.<\/p>\n

    What are the sources of this Data?<\/strong><\/p>\n

    This happens because of digital footprint<\/strong> (This is not related anywhere to carbon footprint, just in case if you thought so).<\/p>\n

    Before talking about this, we can thank the Government<\/strong> for Digitalization<\/strong> and Jio<\/strong> for Mobile Data.<\/p>\n

    With so much consumption of data two types of footprints are released.<\/p>\n

      \n
    • \n

      Passive digital footprints<\/h4>\n<\/li>\n<\/ul>\n

      A passive footprint<\/strong> is formed when information<\/strong> is collected<\/strong> from the user without the person knowing this is often happening.<\/p>\n

      A lively digital footprint is where the user has deliberately shared information<\/strong> about themselves either by using social media sites<\/strong> or by using websites<\/strong>. It is collected without the owner knowing (also known as data exhaust<\/strong>) that data about him is getting collected.<\/p>\n

      This type of footprint is stored in an online database as a “hit”<\/strong>. It tracks the user’s IP address<\/strong>. With that, it keeps a hold on the day and time it got created and from where did the data came. This footprint can be stored in files, which can be accessed by administrators<\/strong>.<\/p>\n

      It helps to view the actions performed on the machine, without seeing who performed them.<\/p>\n

        \n
      • \n

        Active digital footprints<\/h4>\n<\/li>\n<\/ul>\n

        Active digital footprints are created when personal data is released deliberately which means he is aware that his actions<\/strong> are recorded<\/strong>. This is done for the purpose of sharing information about oneself by means of websites<\/strong> or social media platforms<\/strong>.<\/p>\n

        Machine learning is smart and it is very simple for the other parties to collect a whole lot of information and come to a conclusion.<\/p>\n

        A lot of information can be gathered of that individual by using simple search engines<\/strong>.<\/p>\n

        An example of a passive digital footprint would be where a user has been online<\/strong> and knowledge<\/strong> has been stored on a web database<\/strong>.<\/p>\n

        2. Abundant data help in recommendations<\/h4>\n

        \u201cWe now have rich data sources to build models that solve problems in high-dimensional space\u201d<\/strong><\/em><\/p>\n

        We all watch Youtube<\/strong> (Netflix<\/strong>, Hotstar<\/strong> or Television)<\/strong> for that matter.<\/p>\n

        During my childhood days, I used to think that the TV and I have a similar liking and all my favorite shows broadcast on it. Little did I know that data was the reason behind it. With the abundance of data, people liking and disliking were all kept in mind before the director thought of making a show. There is an abundance of data right now, and data that is being collected<\/strong> and stored.<\/strong><\/p>\n

        \u201cInformation overload\u201d<\/strong> is happening and quality is the thing which everyone is looking for.<\/p>\n

        So much information spamming us day to day, starting from email<\/strong>, social networking<\/strong>, blogs<\/strong>, podcasts<\/strong> (and the never-ending list). It\u2019s impossible to keep up altogether. But, not anymore.<\/p>\n

        Now, there will be no more concerns about missing useful content and the stress of finding and tracking the best content<\/strong> be there.<\/p>\n

        With Machine Learning methods the tools to locate and recommend the most relevant content is present.<\/p>\n

        So now you can overcome the information overload, take a back seat because everything is sorted(I am just talking about your data :P).<\/i><\/b><\/p>\n

        3. Quantified Self?<\/h4>\n

        In the era of smartwatches<\/strong> and Fitbits<\/strong>, a Casio<\/strong> can\u2019t survive (Because it doesn\u2019t ask you Casi-ho?#Pun Intended, but read twice to understand the meaning). With quantified self tracking your health is possible. Your everyday data is getting collected<\/strong>.<\/p>\n

        Your everyday information like starting from the biological information like heartbeats<\/strong>(Wow!), breaths<\/strong>, steps<\/strong>, to the interactions such as conversations and words spoken by you (Mind is blown :O) are taken a record of.<\/p>\n

        Mobiles are covered in sensors that can monitor<\/strong> orientation<\/strong>, location<\/strong>, audio<\/strong> and video<\/strong> of the surrounding area (you might not like this location feature, but your parents must be loving it. LOL!)<\/p>\n

        These streams of data can meet at confluence points like people<\/strong>, locations<\/strong>, and organizations<\/strong> and questions can be answered that had not even been conceived could be answerable.<\/p>\n

        This is one of the major reasons why machine learning is popular.<\/p>\n

        4. Need Some Motivation? Your Machine is there for Triggering Intervention!<\/h4>\n

        You might not believe me, but your mental state<\/strong> (like lethargy<\/strong>, boredom<\/strong> or procrastination<\/strong>) can be solved. Irrespective of your location (home, office or around the world) you will get triggering interventions<\/strong>. You can\u2019t help it (shouldn\u2019t even try) cause you will get inspiring targeted action<\/strong>. This will help you to optimize your goals like efficiency<\/strong>, effectiveness<\/strong> or productivity<\/strong>. This method provides the capability to model complex problems using large volumes of seemingly disparate data.<\/p>\n

        5. Abundant Computation<\/h4>\n

        Machine learning is popular because computation is abundant<\/strong> and cheap<\/strong>. Abundant and cheap computation has driven the abundance of knowledge we are collecting and therefore the increase<\/strong> in capability<\/strong> of machine learning methods. This is often why there’s an abundance of knowledge and why we’ve more powerful<\/strong> machine learning methods available.<\/p>\n

        This abundant computation also means you’ll write systems that do quite they’re use to.<\/p>\n

        A lot of calculation leads to confusion<\/strong>, frustration<\/strong>, and no solution<\/strong>. It\u2019s true that computation is abundant and it is cheap.<\/p>\n

        So, you can be Aryabhatta<\/strong> too, and with the abundance master the art of structuring. The world has changed and a lot is there to explore. With the powerful computers, you can rent one at cents and run large experiments<\/strong> on immense data sets<\/strong>.<\/p>\n

        Now, with this, you don\u2019t need to write scripts and programs for long runs of algorithms.<\/p>\n

        You now don\u2019t have to think hard about what question you want to answer (like which algorithm is better, and which parameters should be considered). You can write a script<\/strong> or a program<\/strong> and run the experiment overnight.<\/p>\n

        So while you chill or are at work, you can let the computer do the talking. The systems now do more than they used to do. Machine Learning has made everything so cheap<\/strong> that it can actively design systems to syphon cycles away from core activities.<\/p>\n

        The important fact why machine learning is so popular.<\/p>\n

        Machine Learning is the Future<\/h3>\n

        Powerful methods have been developed. The principles are well understood in statistical and probabilistic frameworks.<\/strong><\/em><\/h4>\n

        Technocrats<\/strong> were aware already, but now users are getting aware too. The field has matured a lot in the last decade and has changed a lot in the last few years. We know that Machine Learning is the brainchild of artificial intelligence. It was a collection of methods<\/strong> that learned from data<\/strong> or experience.<\/strong><\/p>\n

        Genetic algorithms<\/strong> and swarm intelligence<\/strong> were considered methods that learn from their environment (How cool is that!).<\/p>\n

        The maturation promoted a statistical<\/strong> and probabilistic<\/strong> underpinning for the methods in the field.<\/p>\n

        So, now the gist that maturation of machine learning brings to us is that in no time it will be a mainstream field and people will work and be dependent on Machine learning.<\/p>\n

        Artificial intelligence will transform the worldwide economy<\/strong>, and AI jobs<\/strong> are in high demand. Getting an education in AI is challenging and requires persistence<\/strong> and private initiative<\/strong>. AI careers are future-proof<\/strong>, meaning they’re likely to survive well into the longer term.<\/p>\n

        There’s an excellent scope of Machine Learning job opportunities in India, and throughout the planet , as compared to other careers. There’ll be 2.3 million jobs in AI and ML by 2022. The ML Engineers draw a high salary that’s 865,257 as per Forbes.<\/p>\n

        Summary<\/h3>\n

        Machine Learning is popular because it helps solve real-world problems faster and smarter than humans. It can handle large amounts of data, find patterns, and give results quickly.<\/p>\n

        The demand for ML jobs and research is high. Companies are looking for people who know how to work with data.<\/p>\n","protected":false},"excerpt":{"rendered":"

        What is Machine learning? How can Machines learn? Machines don\u2019t have brains to learn! Or do they? Do your mind also boggles from curiosity when you hear such terms? It\u2019s okay! After reading this...<\/p>\n","protected":false},"author":5,"featured_media":69395,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[21024,21025,21022,21023],"class_list":["post-69224","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-what-is-machine-learning-so-popular","tag-why-learn-machine-learning","tag-why-machine-learning","tag-why-machine-learning-is-popular"],"yoast_head":"\nWhy is Machine Learning so popular? - From a techno geek's diary - DataFlair<\/title>\n<meta name=\"description\" content=\"why machine learning is gaining popularity. Top reasons to start learning machine learning. 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