Explore 3 Main Machine Learning Algorithms - Clear the Concept

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Introduction

The exciting field of ML is growing in every sector at a rapid pace. Although it is a subset of artificial intelligence, its unique features make it more popular than AI. Businesses must simplify the wealth of data for easy machine learning applications. Thus there are various algorithms that help to simplify the data.

This blog is all about machine learning algorithms and when you should use each of them. 

What is Machine Learning?

The field of artificial intelligence deals with data and algorithms to simulate human learning. ML enables machines to get better over time, getting more precise when making predictions or classifications, or discovering data-driven insights. 

Furthermore, it works by employing a combination of data and algorithms to anticipate patterns and categorise data sets. Then, it uses an error function to assess accuracy. In the end, it optimises the fit of the data points into the model.

Types of Machine Learning Algorithms

There are several ways to classify the different algorithms in machine learning. But generally, you can categorise them according to their intended use. So, the basic categories are as follows:
 

1. Supervised learning algorithms
2. Unsupervised learning algorithms
3. Reinforcement Learning algorithms

1. Supervised learning 

It includes processing in between each input/output pair. This form of ML algorithm feeds historical input and output data into machine learning algorithms. Thus, shifting the model to produce outputs that are as nearly matched to the desired outcome as possible. Neural networks, decision trees, linear regression, and support vector machines are common supervised learning techniques.

There is a reason why this type of ML is called supervised learning algorithms. It is because you feed the algorithm information to aid in learning while it is being "supervised." The result of the information you supply is usable as input features. Whereas the output you give the system is labelled data. 

Semi-supervised Learning

Semi-supervised learning can be seen as an extension of supervised learning. It is a middle ground between supervised and unsupervised learning. It incorporates unlabeled data alongside labeled data to enhance the model's performance. Algorithms for semi-supervised learning can include variations of supervised algorithms that are adapted to handle both labeled and unlabeled data.

2. Unsupervised learning algorithm

Unsupervised learning does not use the same labelled training sets and data as supervised learning. So it does not require humans to assist the machine in learning. Instead, the machine looks for less common patterns in the data.

This type of ML algorithm is useful when you need to find patterns and use data to make judgments. Hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models are common unsupervised learning algorithms. Predictive models are also developed using this form of ML. Additionally, clustering and association, which identify the rules that exist between the clusters, are its common uses. Clustering creates a model that categorises objects corresponding to certain features.

3. Reinforcement Learning

The reinforcement machine learning algorithms aim to use data from interactions with the environment. It guides actions that will either maximise reward or reduce risk. The agent, a reinforcement learning algorithm, continually learns from its surroundings. The agent gradually gains knowledge from its interactions with the environment until it has investigated every possible state.

ML, which includes reinforcement learning, is a subset of artificial intelligence. It enables software agents and machines to automatically decide the best course of action within a situation to maximise performance.

What is a Regression in Machine Learning? 

Regression is a technique for figuring out how independent features or variables relate to a dependent feature. Once you find the link between the independent and dependent variables, you can predict the outcomes easily. Therefore, Regression is a statistical study area that is essential to ML forecast models. It can be used to predict and forecast results based on data. 

Regression works as a method to predict continuous outcomes in predictive modelling. Regression using ML often includes drawing a line of best fit through the data points. Hence, the distance between each point and the line is minimised to obtain the best-fit line.

Machine Learning Models for Classification or Regression

ML models are also crucial like machine learning algorithms. Some common models of machine learning are :
 

1. Support Vector Machine (SVM)

An SVM can classify data by identifying the linear decision boundary (hyperplane). A hyperplane separates the data points of one class from the data points of the other.

2. Decision tree 

A decision tree allows you to find responses to data by tracking the decisions in the tree from the root down to a leaf node. 

3. Generalised Additive Model (GAM)

GAM models combine univariate and bivariate shape functions of predictors to define class scores or response variables.

4. Neural network

A neural network, which takes inspiration from the human brain, consists of interconnected nodes or neurons. They are in a layered structure that links inputs to desired outputs.

Conclusion

There are different criteria to classify machine learning algorithms. We have explained the common types of ML algorithms. So, you can choose according to your project and the data you have. Moreover, you can easily decide if you need to use Supervised, unsupervised, or reinforcement algorithms. It depends on your data, time, expertise, and the accuracy you want.