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Demystifying Machine Learning

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Machine Learning”. Now that’s a word that packs a punch! Machine learning is hot stuff these days! And why won’t it be? Almost every “enticing” new development in the field of Computer Science and Software Development, in general, has something related to machine learning behind the veils. Microsoft’s Cortana – Machine Learning. Object and Face Recognition – Machine Learning and Computer Vision. Advanced UX improvement programs – Machine Learning (yes!. The Amazon product recommendation you just got was the number crunching effort of some Machine Learning Algorithm).

And not even just that. Machine Learning and Data Science in general is EVERYWHERE. It is as omnipotent as God himself, had he been into Computers! Why? Because Data is everywhere!

So it is natural, that anyone who has above-average brains and can differentiate between Programming Paradigms by taking a sneak-peek at Code, is intrigued by Machine Learning.

But what is Machine Learning? And how big is Machine Learning? Let’s demystify Machine Learning, once and for all. And to do that, rather than presenting technical specifications, we’ll follow a “Understand by Example” approach.

Demystifying Machine Learning” is a term used to describe the process of making machine learning more accessible and understandable to a wider audience. Machine learning can be a complex and intimidating field, but with the right approach, it can be made more accessible and understandable.

There are several ways to demystify machine learning, including:

Breaking down complex concepts: Machine learning concepts can be complex and difficult to understand. By breaking them down into simpler components and providing clear explanations, it can be made more accessible to a wider audience.

  1. Using visualizations: Visualizations such as graphs, diagrams, and animations can help to explain complex concepts in a more intuitive way.
  2. Providing hands-on examples: Providing hands-on examples of how machine learning works in practice can help to make it more tangible and understandable.
  3. Simplifying the mathematical notation: Machine learning often uses complex mathematical notation that can be difficult for non-experts to understand. Simplifying the notation and providing explanations can make the concepts more accessible.
  4. Using real-world applications: Explaining how machine learning is used in real-world applications can help to make it more relatable and understandable.
  5. Encouraging experimentation and exploration: Encouraging experimentation and exploration with machine learning can help to demystify it by allowing people to see how it works in practice and gain a deeper understanding of it.

Machine Learning : What is it really?

Well, Machine Learning is a subfield of Artificial Intelligence which evolved from Pattern Recognition and Computational Learning theory. Arthur Lee Samuel defines Machine Learning as: Field of study that gives computers the ability to learn without being explicitly programmed.

So, basically, the field of Computer Science and Artificial intelligence that “learns” from data without human intervention.

But this view has a flaw. As a result of this perception, whenever the word Machine Learning is thrown around, people usually think of “A.I.” and “Neural Networks that can mimic Human brains ( as of now, that is not possible)”, Self Driving Cars and what not. But Machine Learning is far beyond that. Below we uncover some expected and some generally not expected facets of Modern Computing where Machine Learning is in action.

Machine Learning: The Expected

We’ll start with some places where you might expect Machine Learning to play a part.

  1. Speech Recognition (Natural Language Processing in more technical terms) : You talk to Cortana on Windows Devices. But how does it understand what you say? Along comes the field of Natural Language Processing, or N.L.P. It deals with the study of interactions between Machines and Humans, via Linguistics. Guess what is at the heart of NLP: Machine Learning Algorithms and Systems ( Hidden Markov Models being one).
  2. Computer Vision : Computer Vision is a subfield of AI which deals with a Machine’s (probable) interpretation of the Real World. In other words, all Facial Recognition, Pattern Recognition, Character Recognition Techniques belong to Computer Vision. And Machine Learning once again, with it wide range of Algorithms, is at the heart of Computer Vision.
  3. Google’s Self Driving Car : Well. You can imagine what drives it actually. More Machine Learning goodness.

But these were expected applications. Even a naysayer would have a good insight about these feats of technology being brought to life by some “mystical (and extremely hard) mind crunching Computer wizardry”.

Machine Learning : The Unexpected

Let’s visit some places normal folks would not really associate easily with Machine Learning:

  1. Amazon’s Product Recommendations: Ever wondered how Amazon always has a recommendation that just tempts you to lighten your wallet. Well, that’s a Machine Learning Algorithm(s) called “Recommender Systems” working in the backdrop. It learns every user’s personal preferences and makes recommendations according to that.
  2. Youtube/Netflix : They work just as above!
  3. Data Mining / Big Data : This might not be so much of a shock to many. But Data Mining and Big Data are just manifestations of studying and learning from data at a larger scale. And wherever there’s the objective of extracting information from data, you’ll find Machine Learning lurking nearby.
  4. Stock Market/Housing Finance/Real Estate : All of these fields, incorporate a lot of Machine Learning systems in order to better assess the market, namely “Regression Techniques”, for things as mediocre as predicting the price of a House, to predicting and analyzing stock market trends.



  1. Increased understanding: By demystifying machine learning, more people will be able to understand how it works, what it can do, and how it can be applied in various fields.
  2. Greater accessibility: Making machine learning more accessible and understandable can lead to more people becoming interested in the field, and more people being able to use and benefit from it.
  3. Improved communication: By breaking down complex concepts and simplifying notation, it becomes easier for experts and non-experts to communicate and work together effectively.
  4. Better decision-making: By providing a deeper understanding of machine learning, it can lead to more informed decisions about how to use and apply it.
  5. More innovation: Encouraging experimentation and exploration with machine learning can lead to new and innovative applications of the technology.
  6. Better education: By demystifying machine learning, it can be more easily incorporated into educational curricula, making it more accessible to students and providing them with valuable skills for the future.
  7. Wider adoption: Demystifying machine learning can increase its adoption across different industries and sectors, leading to more widespread use and impact.
    Higher efficiency: Improving understanding of machine learning can lead to more efficient and effective deployment, reducing time and resources required to implement solutions.
  8. New opportunities: Making machine learning more accessible can open up new opportunities for research and development, leading to further advancements in the field.
  9. Greater accountability: Improved understanding of machine learning can lead to better accountability and transparency, helping to ensure that the technology is used ethically and responsibly.
  10. More collaboration: Demystifying machine learning can encourage collaboration between different disciplines, leading to cross-disciplinary solutions that tackle complex problems.

So as you might have seen now. Machine Learning actually is everywhere. From Research and Development to improving business of Small Companies. It is everywhere. And hence it makes up for quite a career option, as the industry is on the rise and is the boon is not stopping any time soon.

So, this is it for now. This wraps up our Machine Learning 101. We’ll hopefully meet again, and when we do, we’ll dive into some technical details of Machine Learning, what tools are used in the industry, and how to start your journey to Machine Learning prowess. Till then, Code Away!

This blog is contributed by Sarthak Yadav. If you also wish to showcase your blog here, please see GBlog for guest blog writing on GeeksforGeeks.

Last Updated : 15 Feb, 2023
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