Machine Learning: An Overview

Thank you for staying tuned to Rucha Yantra’s blog series. Previously, we have shared our knowledge and general information on next-gen technologies such as Industry 4.0, IoT, and AI. With this blog, we are starting a series on Machine Learning (ML). First, we shall try to answer the most basic questions like:

  • What is Machine Learning?
  • What is the basic process of ML?
  • What are the major real-world applications of ML?

So, let’s start answering. Hope you find the ML series useful.

What is Machine Learning?

At its core, it is a tool for turning information into knowledge. Today, we are witnessing, with the rise of digital offerings and connectivity, an explosion of data. This vast mass of data is useless unless we use it to improve the offerings and solve real-world problems. Machine Learning is a tool to automatically find valuable underlying patterns within complex data that is otherwise quite difficult.

As the name suggests, Machine Learning algorithms continuously ‘learn’ through the inputs we provide to the system. Using the input data, they discern rules and relationships within the data. As they grow ‘wiser,’ they can answer similar questions automatically and with greater accuracy. In contrast to traditional algorithms wherein humans created rules to find answers to questions, ML uses data and already-available solutions to discover the rules behind a problem.

Basic Terminology and Process of ML

There are broadly three significant terms one should know while getting to know ML:

  • Features: Important pieces of data that describe a particular problem. They are fed into an ML program to help it learn the problem.
  • Dataset: A set of examples of data. These examples contain features essential to solving the problem.
  • Model: It is the output of training an ML algorithm. It is the representation of the relationship that the algorithm discovers within the data.


  1. Data collection: Collecting the data to feed into the algorithm
  2. Data preparation: Extracting important features and engineering the data into an optimal format
  3. Training: The stage where the algorithm learns using the data
  4. Evaluation: Testing the model’s performance
  5. Tuning: Improving and optimizing the algorithm performance.

Where is ML applied currently?

Most of us are unaware that we already interface with ML daily. Let us look at how in five different ways:

1. Social Media Features

We all spend significant time on social media plat forms. We all get suggestions of people and social media pages to follow on Facebook, Instagram, Twitter, etc. But do you how these platforms decide what suggestions to make to you? Enter Machine Learning. It helps such platforms learn about us from our activities, chats, likes, comments, etc., and then identify people and pages to suggest.

2. Product Recommendations

When you buy something on Amazon or Flipkart, you must be getting recommendations of other products to buy. These, too, are powered by ML. E-commerce websites learn about your behavior based on your purchases, search patterns, etc., and then recommend additional products for you to buy.

3. Email Spam and Malware Filtering

While working through our email inbox, we usually see labels such as ‘important,’ ‘normal,’ and spam. Whenever a new mail is received, it is scanned and filtered automatically by an ML algorithm into such folders. As we manually mark our emails, the ML algorithm learns more about our preferences and becomes more accurate in filtering.

4. Predict Potential Heart Failure

ML is hugely impacting medicine and healthcare. Traditionally, a physician would dig through multiple health records of a patient to conclude a diagnosis. However, with ML algorithms, a doctor’s e-notes can be analyzed in seconds to identify patterns in patient history. Such analysis helps in predicting eventualities such as heart failure.

5. Language Translation

We all must have used the online translation tools while communicating in multiple languages, especially when traveling overseas and talking to foreigners. Such translation tools are powered by robust ML algorithms that get better every time we make a query and select the most suitable translation option.

Now that you have seen the most prevalent and real-world examples of how ML is used, you shall be better able to appreciate its impact and growing presence. Rucha Yantra is committed to incorporating technologies such as ML in its products to better serve our customers. In the next blog, we shall delve deeper into ML and look at the different approaches to Machine Learning. Stay tuned to our Knowledge Corner for further blogs!

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