Types of Machine Learning (ML): A Beginner’s Guide

Right from image recognition to fraud detection, there are barely any ways left where the magic of machine learning (ML) and artificial intelligence (AI) has not mesmerized us with. Together, both ML and AI have changed the way we interact with data and use it to enable massive digital growth. On that note, customers too have benefitted from its magic, in identifying data and then using that data to receive accurate outputs. Today, in this blog, we will walk you through the three types of machine learning. But before that, let us brush up on some of the basics.

What is Machine Learning?

Machine Learning is a subset of AI technology that makes predictions and recommendations by processing data and experiences. It enables the machines to develop algorithms and problem-solving models by identifying certain patterns in data. These algorithms then use these patterns to recreate the model and foster a rather accurate output for the customer.

So, how does the algorithm of Machine Learning function? To begin with, the machine learning algorithm is taught by using a training data set to form a model. As and when new input data is fed to this algorithm, it tends to create a prediction on the basis of the model. This prediction is then examined for accuracy. And the second this accuracy is of acceptable standards, the ML algorithm is all set to be deployed.

Now, it can be segregated into many ways, but three major recognized types of machine learning make it prominent: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Let us delve into them with a magnifying lens.

Supervised Learning

Supervised Learning is an algorithm that uses training data and feedback from humans to learn the relationship of given inputs and translate to a desired output. Supervised learning can be applied to gain foresight, recognize data or classify it. The algorithm is trained out of the data to discover the relationship between the input and output variables. Once this training is complete— typically when the algorithm is sufficiently accurate, the algorithm is then applied to newer data.

Sample Business Use Cases in Customer Success

Supervised Learning can be deployed in the customer success niche in a couple of ways. It helps the niche in the following manner:

• To understand product-sales drivers such as competitive prices, distribution, advertisement, etc.
• To categorize customers based on how likely they are to repay a loan
• To predict customer churn
• To forecast a sales lead’s likelihood of closing
• To render a decision framework for hiring new employees
• To analyze product attributes that make a product most likely to be purchased
• To optimize price points and estimate product-price elasticities

Unsupervised Learning

Unsupervised learning is a special type of machine learning which is the rear opposite of Supervised Learning. It has been programmed to create predictive models from data that constitutes of input data without historical labeled responses. Unsupervised learning can also be deployed to develop data for further supervised learning.

This is done by filtering patterns or features that can be used to segregate, reduce and lessen the volume of data present. As unsupervised learning relies upon the data and its properties, we can safely assume that unsupervised learning is data-driven. The outcomes from an unsupervised learning task are controlled by the data and the way it is formatted.

Sample Business Use cases in Customer Success

• To segment customers into groups by distinct characteristics such as age group
• To segment customers using less-distinct customer characteristics such as product preferences
• To segment employees based on the likelihood of attrition
• To cluster loyalty-card customers into progressively more micro-segmented groups
• To inform product usage/development by grouping customers mentioning keywords in social-media data.

Reinforcement Learning

In reinforcement learning (RL), is a type of machine learning where the algorithm produces a variety of outputs instead of one input producing one output. It is trained to select the right one based on certain variables. It is an algorithm that performs a task simply by trying to maximize rewards it receives for its actions. Further, it lets the machines to naturally detect the ideal behavior within a specific context that triggers the machine to maximize its performance. Simply put, it is based on rewarding desired behaviors or punishing undesired ones. The Reinforcement Learning goes via the following steps:

1. Input state is observed by the agent.
2. Decision making function is used to make the agent perform an action.
3. After the action is performed, the agent receives reward or reinforcement from the environment.
4. The state-action pair information about the reward is stored.

Sample Business Use cases in Customer Success

• To revamp resource management: Reinforcement learning is deployed in Google’s data centers to satisfy our power requirements efficiently, thereby, cutting major costs.
• To personalize suggestions to the customers as has been used by Facebook.
• To deliver more meaningful notifications to the customers.

Supervised Learning vs. Unsupervised Learning vs Reinforcement Learning

Let us now look at how the three types of Machine Learning – Supervised Learning, unsupervised Learning and Reinforcement Learning differ from each other. A report by McKinsey puts some spotlight on this:

Parting Thoughts

As we have successfully tracked down the three types of machine learning and their respective use cases, it is imperative to state that there are a lot of instances where the differences blur out. Fortunately, our world is slowly changing with the benefits of machine learning and is constantly getting more prevalent in our everyday lives. This will help us understand the basics as well as the advanced versions of the technology that we use or will use in the near future to come.

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