The emergence and evolution of data science have been one of the biggest impacts of technology on enterprises. As the web world keeps growing and getting competitive, there’s a dire need for businesses to learn as much as they can about their consumers and the patterns impacting sales and profits. In short, there’s a need to implement technologies that can simplify data gathering, assorting, and managing, now more than ever.

That’s where Machine Learning (ML) comes in, the bleeding-edge technology that is garnering so much attention. But in spite of being a coming-of-age 21st-century technology, ML remains a largely misunderstood area. Non-technical people often confuse it with Artificial Intelligence (AI).

And that consists of our first point of demystifying Machine Learning. Yes, that is what we are going to do in this article. We are going to demystify ML, especially for the budding business’ Beethoven(s) who are inclined towards inculcating this technology into their business apps and operational systems but lack a clear idea.

ML is a vast theme and given it has a well-entailed technological aspect and of course, a wide spectrum of industrial benefits, we will cover the topics that are required to have a good grasp of it including its many advantages. So let’s begin with the most obvious question.

Machine Learning is a part of AI that involves the use of computational statistics, algorithms, and smart mathematical optimization to make software systems smarter and more accurate at predictions. By gathering historical data (both labeled and unlabeled data) and analyzing it with the help of algorithms, the software can make classified predictions providing useful insights for businesses. Examples of utilization of ML include speech recognition, automated query responses, email filters, and refined search engine results.

The term Machine Learning was coined by Arthur Samuel in 1959 while he was working at IBM. According to him, the chief purpose of ML was to simplify and automate operations that granted computers their ability to learn without explicit programming and improve the overall user experience.

As new apps, businesses and technologies keep emerging, there is, indeed, a dire need to enhance user experience, and ML is doing pretty well in that department. In fact, the survey reports published on ML by the German company Statista forecast that by 2025, the ML market is estimated to grow from 22.6 billion U.S. dollars to almost 126 billion U.S. dollars. It also states that ML constitutes the largest segment of the AI market.

Just like an app, a website or an eCommerce portal, ML also functions on the accumulation of different tools and technologies. The most basic tool that drives ML is an algorithm. When users give their inputs, the ML algorithms track the data and try to trace the patterns, and respond accordingly (query responses and search keyword suggestions).

ML chiefly involves two types of data — labeled and unlabeled. A labeled set of data is one in which data samples are tagged with informative labels. For example, for search features that enable searching with a photo, labeled data may contain an array of tagged photos like household items, apparel, name of fruits, plants, etc. So when a user scans a picture of a blender, the algorithm immediately identifies the input based on the labeled data and returns filtered results.

Unlabeled data on the other hand is the one where data samples aren’t tagged. In this case, the ML model has to go evaluate each piece of the given input. For example, if a user enters the picture of an apple, but if it’s unlabeled, then the algorithm will evaluate each and every aspect of the picture like its color, shape, and other characteristics to determine the fruit and return accurate search results.

Machine Learning models are classified into four broad categories depending on the type of dataset and the corresponding algorithm designed for task automation and data classification. Let’s have a quick look.

1. Supervised Learning

Supervised learning is the most common ML model and includes working on labeled data. The algorithm designed for supervised learning is trained to map the inputs and produce relevant outputs. Since the data here is already trained or supervised to learn, predict and return expected results, hence the term supervised learning. An example of supervised learning is sorting emails into proper categories based on their senders. That’s why your Gmail app is able to receive and sort emails into Primary, Social and Promotions.

2. Unsupervised Learning

In an unsupervised learning model, the algorithm has to train itself by analyzing, processing and clustering unlabeled data. This means that the algorithm has to iterate the inputs and discover the hidden patterns in the datasets without explicit programming. Whenever the algorithm comes across a new set of data, it tries to identify the commonalities of the given data and return results accordingly.

An online retail app sets a good example of unsupervised learning where the app classifies suggestions based on the profile of users, their browsing patterns and purchased items.

3. Semi-supervised Learning

This ML model blends the techniques of the above two. A semi-supervised learning model is trained by using a smaller set of labeled data but giving it enough freedom to explore the new pieces of data and develop an understanding of its own.

In fact, the labeled dataset of the algorithm provides direction and empowers it to extract information from the larger set of unlabeled data. The practical applications of this model are found in speech analysis apps and in web content categorization.

4. Reinforcement Learning

Reinforcement learning is another popular ML model and works on a concept similar to supervised learning. But instead of using labeled datasets, the model is made to learn via trial and error. The algorithms designed for this model employ dynamic programming techniques. Some gaming apps use this learning model while playing against a human opponent. Autonomous vehicles also make use of this learning model.

It’s no secret that ML is a ubiquitous technology. From the moment you open your favorite shopping app to the time when you’re using a text editor, it’s right there. ML delivers a unique experience to users and at the same time empowers businesses with actionable data and insights.

It holds immense potential for businesses, especially start-ups that can influence their potential customers with personalized marketing, increase sales by knowing where to focus, and address the under-performed areas. Some of the business benefits of ML include:

  • Making informed and better decisions with the help of accurate data
  • Personalized product recommendations and increased customer satisfaction
  • Assistance in making dynamic pricings like price hikes in car rental apps during poor weather conditions or deciding on product pricing after comparison with local and international brands
  • Streamline production by having better forecasts of market demand and supply
  • Increased efficiency and productivity with automated operations and reduction in manufacturing defects
  • Efficient financial management with accurate estimations of business expenses and cost analysis

Machine Learning has come a far way but it is still evolving, and it’s way more complicated in technicalities. Sure it can help start-ups make smart decisions and help with resource management with accurate predictions, but to ensure its optimum utilization, one will need to hire dedicated developers who are well-versed with the technology and know what will work for what. Only then can one reap the many benefits of this modern-era tech.

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