Types of Machine Learning: A Beginner’s Guide

Machine learning is not magic. It’s just a tool. But it’s an important tool, says Greg Corrado, a senior research scientist at Google.

Based on a new report by Grand View Research, Inc. by 2025 the machine learning market is expected to rise to USD 96.7 billion. Also, the market’s CAGR is expected to expand by up to 43.8% from 2019 to 2025. Precisely, technologies such as machine learning are now being adopted widely by almost every industry out there.

Machine learning extensively helps detect meaningful predictions within datasets. Various machine learning algorithms that are software-based such as fraud detection, anti-spam software, and search engines are being used extensively by most industries, thus contributing to their economic growth.

Machine learning is a form of artificial intelligence that makes use of algorithms enabling a system to learn from data without any human intervention. It follows the process of data preparation, training an algorithm, generating a machine learning model, and finally making and refining positive predictions.

Machine learning can be categorized into three types: –

1. Supervised machine learning:

Supervised machine learning simply indicates learning where the machine is taught or trained using data that will be labeled, meaning the data will already be tagged with the right answer. After which the machine is given a new set of data for the supervised learning algorithm to analyze the trained dataset that gives the right and relevant outcome from the data that is already labeled.

Two types of supervised machine learning include categories of algorithms which are: –

• Classification – this classifies a problem when the output variable comes under such categories i.e. green or blue or disease or no disease
• Regression – this classifies when the output variable consists of a real value i.e. weight or dollars

2. Unsupervised machine learning:

Unsupervised learning is just the vice versa of supervised learning. In short, it indicates the learning or training of a machine that uses neither labeled nor classified information allowing to act on the data or information without any kind of interference.

This is unlike supervised machine learning where training is given to the machine. Rather there’s a restriction for the machine to find the hidden structures in unlabeled data by ourselves.

Let us quote an example, imagine the machine provides images of cats and dogs that it has never seen. Thus said, the machine has no clue regarding the features of these animals. Hence, the machine will not be able to identify them as cats or dogs. However, their similarities can be recognized based on their patterns, differences, and similarities, etc.

This can be categorized into two different categories such as: –

1. Association – it is a rule problem you need to discover rules such as the ones that describe large sections of the data you’ve gathered. For instance, people that purchase an A item also have the probability of purchasing the Y item
2. Clustering – this takes place when you want to identify the inherent grouping in the data. For instance, identifying customers through the purchasing behavior

3. Reinforcement learning:

Reinforcement learning helps in training the algorithm to learn and interact with algorithms based on rewards or punishment.

This reinforcement learning learns in a manner like how a kid learns to perform a new task or take up a new responsibility. However, this is in contrast with other machine learning approaches out of which this algorithm does not explicitly tell you how to perform a certain task, however, it works on its problems.

There exist two types of reinforced machine learning: –

• Positive reinforced machine learning – this takes place when there’s a specific behavior that takes place
• Negative reinforced machine learning – this happens when there’s a negative strengthening of behavior