Grease the wheels of Machine Learning
By: Sivashankari Vaitheswaran
Tags: Machine Learning, AI, Techology, Computer Science, Data Science

Machine Learning has become the most-talked-about topic these days, what is so different that Machine learning does when compared to traditional software applications?

In the traditional software applications, we developed systems and logic for existing rules and we kept updating the systems as rules changed. But, change is the norm in business and technology. The main feature of machine learning is that it allows you to continually learn from data and predict the future. This powerful set of algorithms and models are being used across industries to improve processes and gain insights into patterns and anomalies within data.

So, what exactly is Machine Learning?

Machine Learning is a field of study that gives computers the ability to learn without explicitly being programmed. Machine learning is a technique that teaches computers to do what comes naturally to humans and animals - learn from experience.

To put this in simple terms,

Consider a 2–3-year-old kid, you would have to teach them how to read & write alphabets and basic rules of the language, vocabulary. After a few years, the kid learns how to read and write without much help. So, you are training the kid to be able to read/write without getting much help in a few years. This is exactly what Machine Learning is

Machine Learning is used in our everyday lives, for example, Google Maps, Product recommendations in Amazon, Alexa, Siri, Facebook product suggestions, Snapchat filters, Instagram’s auto-recommended emojis, The image search in Google Photos that enables you to find the species of plant/insect and many more.

How does Machine Learning do what it does?

Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases.

A Machine Learning program is said to learn from Experience ‘E’ with respect to some Task ‘T’ and Performance measure ‘P’. The performance of the program improves with experience ‘E’ for the task ‘T’, performance measured by some parameter ‘P’.

Basically what this means is that,

the computer program learns from experience and gets better as it experiences more such incidents by accumulating more data.

Categories of Machine Learning:

Machine learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories of machine learning.

Supervised Machine Learning algorithms

In Supervised Machine Learning, the algorithm applies what it has learned from previous experience and produces an inferred function to make predictions. The model compares the output of the existing data set and compares it with the intended and the calculated output and rectifies the errors. The disadvantage of this system is that the data needs to be labeled.

Unsupervised Machine Learning algorithms

Unsupervised learning studies the hidden patterns in the unlabeled data set and builds a function. The system does not figure out the right output, but it explores the data and can draw inferences from datasets to find the hidden structures in unlabeled data. This is used when the dataset that is intended to train has neither been classified nor labeled. The disadvantage of Unsupervised algorithms is that the application spectrum is limited

Semi-supervised Machine Learning algorithms

As the name suggests, Semi-supervised machine learning algorithms are a mix of both Supervised and Unsupervised algorithms. This was developed to overcome the limitations of both Supervised and Unsupervised learning algorithms. This typically has a very small amount of labeled data and a huge amount of unlabeled data.

Reinforcement Machine Learning algorithms:

Reinforcement Learning does not require inputs or outputs. All this does is that it learns from the environment by trial and error. This method allows machines to determine the ideal behavior within a specific context to maximize its performance. Few applications of this method include: game theory, aircraft control, business strategy planning

Why is Machine Learning so popular?

The amount of data that is generated per day on the internet is almost 2.5 quintillion bytes, the amount of data that is generated has steadily been increasing over the years. To process unstructured data from the internet and make it structured to conclude and valid information from it is a huge task. Blunders like missing data, wrong interpretation can cause the business a huge loss. To overcome this, Machine Learning can be used.

Machine learning enables the analysis of massive quantities of data. It generally delivers faster, more accurate results to identify profitable opportunities or dangerous risks. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information. Machine Learning aims to build a model that does the task without very little to no human intervention.