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Demystifying Machine Learning

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Demystifying Machine Learning

Machine Learning: This is a powerful term! Machine learning is the hottest topic these times! Why shouldn’t it be? The majority of “enticing” new development in Computer Science and Software Development generally has something connected to machine learning hidden behind a veil. Microsoft’s Cortana – Machine Learning. Object and Face Recognition – Machine Learning and Computer Vision. The most advanced UX improvement programs include Machine Learning (yes! The Amazon product suggestion we received is the result of the number-crunching efforts of a Machine Learning Algorithm).

It’s not only that. Machine Learning and Data Science generally are everywhere. Why? Because data is everywhere!

Therefore, it’s only natural that someone with an above-average brain and can distinguish between Programming Paradigms by looking at Code is enthralled at the prospect of Machine Learning.

What do we mean by Machine Learning? And how big is Machine Learning? Let’s explore Machine Learning, once and for all. Instead of presenting the technical specs, we’ll use the “Understand by Example” approach.

Machine Learning: What is it really?

Machine Learning is a subfield of Artificial Intelligence that evolved from Pattern Recognition and Computational Learning theory. Arthur Lee Samuel defines Machine Learning as a field of study that provides computers with the ability to learn without needing to code explicitly.

This area is Computer Science and Artificial intelligence, which “learns” by studying data without human intervention.

However, this notion is not without flaws. Due to this belief, when the term Machine Learning is thrown around, it is usually thought of as “Artificial Intelligence” as well as “Neural networks that are able to emulate Human brains (currently it isn’t possible)” or self-Driving cars and so on. However, Machine Learning is far beyond the scope. We will explore some typical and some not generally thought of aspects in Modern Computing where Machine Learning is at work.

Machine Learning: The Expectated

Let’s start by highlighting some areas in which Machine Learning plays a role.

  1. Speech Recognition (Natural Language Processing in more technical terms):
    We communicate with Cortana through Windows Devices. How does it comprehend what we’re saying? The field of Natural Language Processing, or N.L.P. It is the study of the interactions among Machines and Humans through Linguistics. The centre of N.L.P. is Machine Learning Algorithms, and Systems (Hidden Markov Models being just one).
  2. Computer Vision:
    Computer Vision is a subfield of Artificial Intelligence which studies the Machine’s (probable) perception of the Real World. This means that the entirety of Facial Recognition, Pattern Recognition Character Recognition techniques are part of Computer Vision. Additionally, Machine Learning, once again, with its broad range of Algorithms, is at the centre of Computer Vision.
  3. Google’s Self Driving Car:
    Well, it’s possible to imagine what it is that drives car. Further Machine Learning goodness.
    These were not necessarily new applications. Even the most sceptical of people would have an understanding of these technological feats that were brought to life by certain “mystical (and extremely difficult) mind-boggling Computer magic”.

Machine Learning: The Unexpected

Let’s see some fields where people who don’t normally connect easily to Machine Learning:

  • Amazon’s Product Reviews: We might have wondered why Amazon always offers a suggestion that entices you to reduce your spending. It’s machine-learning Algorithm(s) known as “Recommender Systems” that is working behind the scenes. It analyses each user’s preferences and provides suggestions based on them.
  • YouTube/Netflix: They function exactly like the above!
  • Data Mining or Data Mining / Big Data: This may not come as an astonishment to some. Machine Learning is lurking nearby if there’s a purpose of obtaining the information out of data. However, Data Mining and Big Data are just a way of learning and studying the data at a greater size.
  • Real Estate, Stock Markets, Housing Finance: All of these fields make use of a number of Machine Learning systems in order to be able to assess the market, specifically “Regression Techniques”, for things as basic as predicting the value of a House or studying trends in the stock market.

Now, that we may have noticed, Machine Learning is everywhere. Everything from Research and Development to improving the business for Small Companies. It’s all over. This makes for a great career opportunity since the field is growing and is that will not end anytime very soon.

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