Home » Types of Machine Learning

Types of Machine Learning

Spread the love

Have you heard about machine learning? If yes, you might be interested to know more about it by understanding the different types of machine learning. 

This article highlights the types of machine learning along with their applications. 

In our day to day lives, we see applications and advancements in several technological fields.

These upgrades in scientific applications and research of the creation of something new has benefited the human race in today’s era compared to the bygone era.

Types of Machine Learning

Machine learning has made our lives easier, reliable and convenient. It is used in almost everyday tasks of our lives and has become an integral part of living. 

Have you wondered how different types of machine learning and its applications are all associated with these? 

For example, in banking industries, Machine Learning in healthcare services, transportation services, educational institutions to digital platforms for virtual payments, online shopping and much more, machine learning plays a vital role in each sector. 

There’s so much of its contribution yet many don’t know what machine learning actually is. 

The study or knowledge of algorithms and statistical aspects of a computer program which they run with no or negligible assistance from humans. 

Machine learning, along with deep learning, is a subset of artificial intelligence. However, one major aspect that distinguishes both of them is that machine learning is probabilistic and deep learning is deterministic. 

Machine learning is evolving in nature as it works beyond the programs it is meant to perform despite being a subset of AI. 

It can gather a huge amount of data to understand a user’s preferences based on their previous patterns and records. 

Almost all the top companies in this world like Facebook, Snapchat, Instagram, YouTube, Google and so on use machine learning to determine customer buying preferences and their patterns of deciding purchases. 

There’s more to learn and understand about machine learning. In order to know more, it’s important to know about the types of machine learning and their applications. Let’s dive right into it!

Types of Machine Learning:

There are three known types of machine learning. They are as follows

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Likewise, with any methods of machine learning mentioned above,  there are various ways of preparing machine learning algorithms, having their own benefits and disservices. 

To comprehend the upsides and downsides of each sort of machine learning, we should initially check out what sort of information they ingest. 

Machine learning has two sorts of data known as labelled data and unlabelled data. 

Labelled data consist of both the information of input and output data in a totally machine-discernible manner, yet requires a ton of human assistance to label the information. 

Unlabeled data just have one or none of the above data in a machine-comprehensible structure. 

This invalidates the requirement for human assistance yet requires more perplexing arrangements. 

Recommended: Python Libraries for Machine Learning

Supervised Learning:

Supervised learning is one of the three types of machine learning. 

It consists of giving accurate input information just as right output information to the ML model. 

The main point of supervised learning calculation is to observe a planning capacity to plan the input variable(x) with the output variable(y).

In supervised learning, the machines use sets of labelled training data and on-premise of that information, machines anticipate the output.  

Labelled data implies there’s already some input data that are fitted together with the right output.

Additionally, in supervised learning, the machines are provided with the set of training data beforehand which enables the machines to anticipate the output accurately. 

To put it better, we are elaborating the steps associated with Supervised Learning below – 

  • Determination of training dataset type. 
  • Collection of the labelled training data. 
  • Dividing the given dataset into three sections : training dataset, test dataset and validation dataset. 
  • It’s important to determine the input of the training dataset so that the output prediction is correctly given by the model. 
  • Knowing the correct algorithm like support vector machine, decision tree and so on for the model. 
  • While we perform the algorithm on the training dataset, there may be a requirement of control parameters for validation as it’s a part of training datasets. 
  • Lastly, the accuracy of the model is determined by the output; if the prediction of output is accurate then the model is successful (accurate). 

Supervised learning is further divided into two types for data mining purposes. They are known as regression and classification. 

  • Regression 

Regression is a process that is usually used to analyse the projection of sales of a particular business. 

It involves dependent and independent variables whose relation and dependency on each other is studied by machine learning experts. 

Some commonly known and used regressions are linear regression, logistic regression, and polynomial regression. 

  • Classification

Classification helps in accurately categorizing test data into their respective categories through the means of some algorithms. 

This happens as the classification process involves identifying particular data information in a dataset and labelling them based on that. 

Some of the algorithms of classification are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbours, and random forest. 

Applications of Supervised Learning:

Supervised learning is highly essential for determining authenticity in today’s era. Hence, it is used in various applications. 

Some of the prominent applications of Supervised Learning are as follows – 

  • Bioinformatics

The storing of human features like fingerprints, iris texture, earlobe, etc that helps in the recognition of a particular person for a particular purpose is known as bioinformatics. 

Bioinformatics is often used for security purposes to protect the privacy of individuals and their belongings. 

Once the bioinformatics of an individual is stored, no other individual can temper, replace, steal, misuse or do fraudulent activities on behalf of the person whose information is recorded. 

One fine example that explains the importance of bioinformatics is fingerprint sensors on Android and iOS phones. 

An android or iOS system captures or stores the fingerprint of the owner. This way the owner can only open the phone or perform other activities as any fingerprint rather than him will automatically get rejected by the system. 

  • Face Recognition

A facial recognition framework uses bioinformatics to store or recognise facial highlights from a photo or video. It contrasts the data and a data set of prior countenances to track down a match

Many security services in homes, offices, phones, etc use face recognition that helps in giving extra miles of protection. 

  • Recommendation engines

Recommendation engines access the history of a user’s previous experiences in searching, selecting, deciding and buying factors. 

To do so, they mostly rely on caches, cookies and frequent search questions along with their results. 

This is because it helps in providing the best user experience to a customer by already displaying his or her general preferences without consuming their time further.  

Some of the other applications of supervised learning are fraud detection, spam filtering, assessment of risk and so on. 

Unsupervised Learning:

In most textbooks and guides, unsupervised learning is defined as the following statement – 

“Unsupervised learning is a type of machine learning in which models are trained using an unlabeled dataset and are allowed to act on that data without any supervision.”

This means unsupervised learning uses AI (machine learning)calculations to dissect and bunch unlabeled datasets. 

Such algorithms find stowed examples or information groupings without the requirement for assistance from men. 

Its capacity to find similarities and contrasts in data make it the best answer for exploratory information examination, strategically pitching methodologies, segmentation of customers, and visual recognition. 

Hence, unsupervised learning can’t be used directly in problems related to classification and regression in light of the fact that contrary to supervised learning, we have the input data yet no related yield output.

The process of unsupervised learning can be compared to a human brain while learning new things or concepts. 

Unsupervised learning is characterized into two classifications of algorithms: 

  • Clustering

A clustering issue is a place where you need to find the innate groupings in the data provided, like gathering clients based on their buying conduct. 

  • Association 

In this type of algorithm issue, you need to find rules that depict enormous bits of the data, for example, individuals that purchase A likewise will generally purchase B.

Some of the most used and popular algorithms of unsupervised learning are as follows – 

  • K-means clustering
  • KNN (k-nearest neighbors)
  • Hierarchical clustering
  • Anomaly detection
  • Neural Networks
  • Principal Component Analysis
  • Independent Component Analysis
  • Apriori algorithm
  • Singular value decomposition

Unsupervised learning is always preferred in performing difficult tasks compared to supervised learning as there are no prior sets of labelled data input. 

This is because unlabelled data is easier to obtain than labelled data. 

Applications of Unsupervised Learning:

Some of the applications for unsupervised learning are as follows – 

  • Recommendation Engines

Recommendation engines help in effective cross-selling strategies by analysing the history of a customer’s preferences and buying patterns. 

This way the customer gets desirable options to consider for purchase during the checkout process in digital shopping platforms.  

  • Detection of unusual activities

One speciality of unsupervised learning models is that it can analyse a large set of data quickly and tell if there’s any kind of anomalies in the given datasets. 

This way, one can easily detect and identify security breaches, fraudulent activities, dysfunctional settings and so on.  

  • Categorization in news sections

People are more inclined towards online news portals than traditional newspapers due to their busy schedules as it’s convenient and time-saving. 

Most people rely on Google News for their daily information. 

To make things reliable and easy, Google uses unsupervised learning to gather articles based on a particular topic from various online news portals and put them under the same section. 

For example, news related to sportspersons like Cristiano Ronaldo or Lionel Messi will be under the section ‘Football’. 

Reinforcement Learning:

Reinforcement learning, also known as semi-supervised learning, is the preparation of machine learning (AI) models to settle on a grouping of choices. 

The specialist figures out how to accomplish an objective in a questionable, possibly complex situation. 

In this type of learning, the AI faces a kind of circumstance where the PC uses experimentation to concoct an answer for the issue. 

To get the machine to do what the developer needs, the AI gets either rewards or punishments for the activities it displays. It will likely amplify the absolute prize. 

Albeit the architect sets the prize approach, he gives the model no clues or ideas for how to address the issue. 

It’s dependent upon the model to sort out some way to play out the assignment to expand the prize, beginning from absolutely arbitrary preliminaries and wrapping up with modern strategies and abilities beyond human assistance. 

By using the force of search and numerous preliminaries, reinforcement learning is presently the best way of implying a machine’s innovativeness. 

As opposed to people, machine learning can assemble insight from a great many equal ongoing interactions if a reinforcement learning calculation is run on an adequately incredible PC framework.

Reinforcement learning is categorized into two classifications. They are as follows – 

  • Positive

Positive reinforcement is characterized as when an occasion, happens because of specific conduct, expands the strength and the recurrence of the conduct. All in all, it positively affects conduct. 

There are some benefits of reinforcement learning, for example, it expands the presentation or performance and supports change for a significant stretch of time. 

Be that as it may, there is a hindrance too as over-burden support can prompt over-burden of states which can lessen the outcomes. 

  • Negative  

Negative Reinforcement is characterized as fortifying of conduct on the grounds that a negative condition is halted or kept away from. 

There are a few benefits of negative support learning, for example, it expands conduct and gives rebellion to the least norm of execution. 

Likewise, the detriments incorporate giving barely enough to get together the base conduct

Applications of Reinforcement Learning:

This type of machine learning has the following applications 

  • Finance and trading

An RL specialist can conclude undertakings like trading and finances like whether to hold, purchase, or sell any assets and stocks. 

The RL model is assessed by using market benchmark guidelines to guarantee that it’s operating ideally. 

This ML process carries consistency into the cycle, not at all like past techniques where investigators would need to settle on each and every choice.

  • Medical services

In medical services, patients can get therapy from arrangements gained from RL frameworks. 

RL can observe ideal approaches using past encounters without the requirement for past data on the numerical model of organic frameworks. 

It makes this methodology more relevant than other control-based frameworks in medical care. 

RL in medical services is classified as dynamic treatment regimes (DTRs) in constant illness or critical situations, robotized clinical analysis, and other general causes 

In DTRs the input is a bunch of clinical perceptions and appraisals of a patient. 

The yields are the treatment choices for each stage. These are like states in RL. 

The use of RL in DTRs is beneficial in light of the fact that it is fit for deciding the best treatment for a patient at a particular time. 

The application of RL in medical services additionally empowers the improvement of long haul results by considering the postponed impacts of therapies. 

RL has likewise been used for the revelation and age of ideal DTRs for chronic conditions.

  • Natural Language Processing

Reinforcement learning is generally used as text summarization, question answering, and machine translation algorithm in Natural Language Processing. 

Other than these, reinforcement learning is also highly applied in robotics, engineering, training systems, data processing and many more. 

Conclusion:

To conclude the article, in short, there are three types of machine learning – supervised, unsupervised and reinforcement learning. 

Supervised learning has labelled data while unsupervised learning doesn’t have labelled data which makes it ideal for complex operations. 

However, in reinforcement learning, the computer is left to solve the problem on its own to strengthen its strategies and techniques. 

Each of these is used in various fields and applications. 

Let us know in the comments below if you would like to share your ideas with us.

Other Related articles:

Leave a Reply

Your email address will not be published.