We hear about artificial intelligence and machine learning interchangeably. And some believe that the two terms carry the same meaning. But that isn’t true. Machine learning is the subset of artificial intelligence. And machine learning algorithms specifically aim at training generative models. 

Businesses and individuals deal with a large pool of data. Machine learning algorithms put these data to the best use. Helping systems improve their capabilities, these algorithms practice constantly to become accurate over time. 

So, let’s understand the different kinds of algorithms and how they work.

What Is Machine Learning?

Machine Learning, a part of AI, involves learning through experience for predicting accurate results.

The AI models are provided with training datasets. Through the help of generative deep learning algorithms, these AI models change their parameters (fed as training input) to produce desired output. 

In short, machine learning aims at utilising tons of data to start imitating the ways humans think and execute tasks. Eventually, assisting the AI tool improve its efficiency itself by repeatedly performing certain actions. 

Types of Machine Learning Algorithms

Although there are various iterations of machine learning algorithms, these fall into three basic categories.

Supervised Algorithm

As the name suggests, a supervised algorithm requires assistance. Basically, this algorithm makes use of a labeled dataset. 

To guide the AI tools to predict accurate responses, the algorithm feeds the model with training and test dataset. While the training dataset specifies the problem, the test dataset helps in validating AI models. 

For instance, let’s think that you wish to target the Facebook users who are most likely to buy your product. In this case, we can utilise the past marketing campaign data for those who saw our product ads. And, the ones who bought our products after checking the ads. 

Using the details, we can create a training data set with various data points about the recipients of our ads and those who purchased. Such as location, age, demographics, interests and more. This will become our label. 

This allows the AI model to try multiple iterations for predicting the label on the basis of the recognised data points. It keeps trying to predict the responses until it finds the best one. 

How Does a Supervised Algorithm Work?

As discussed, when training a model using a supervised algorithm, the output is already known. Hence, the model is provided with huge training input data (consisting of input and its corresponding output values).

Through multiple iterations of predictions, the model becomes accurate overtime. Once the training dataset is exhausted, the model can be fed with another set.  

Different Supervised Learning Algorithms

Classification: Image classification, decision trees are a few types of problems that come under classification. Here, the predictions are classified as specific classes. For instance, just a “yes’ or “no”. 

Depending on the number of classes, the problem is further divided into two types. Binary Classification when there are 2 class values and Multi-class Classification for values exceeding 2 in number. 

These classes or the responses are discrete values. For example, if the image is an apple or a mango? Through prediction, the image is specified to a class.

Regression: Regression algorithms help predict stock prices, home prices, monthly sales and similar problems. Thus the responses are continuous values. For instance, the sales ranging from A to B. 

In addition, the algorithm can take multiple values. Hence, the algorithm predicts results based on nuances of parameters. For instance, to predict the house rent, a regression algorithm can take up parameters such as location, nearby amenities, property size and more.

Unsupervised Algorithm

This is completely opposite of the supervised learning algorithm as it works with an unlabeled dataset instead of labeled data set. The unsupervised algorithm finds hidden patterns with the available data to predict the most accurate output. Hence, this is helpful when you aren’t sure what to look for.

So, how does an unsupervised algorithm be helpful? Dealing with a large database could be frustrating. However, when clustered in a group, it becomes easy to manage the datasets. But what if you do not know how to cluster the large database into smaller segments. 

The best way to identify the basis of the segmentation is to use an unsupervised algorithm. For instance, let’s imagine you own a car dealer, selling vehicles from different brands. You want to group these vehicles to make it easier for your customers to look through the options. However, you are not sure about the apt data point for clustering the cars. Whether type, brand or maybe some other parameter. 

In such a case, using an unsupervised algorithm is very helpful. It could check for the patterns from your customer data to identify the most accurate data point for grouping your cars. 

How Does an Unsupervised Algorithm Work?

In absence of any logical connection between the input and output values, the unsupervised algorithms use various techniques to mine data patterns, and groups data into similar types. Using these groups, it becomes simpler to understand data and identify related output.

The input dataset, in case of unsupervised algorithms, isn’t structured. Instead, it might carry noisy data, outlines etc. Through model training, these inputs are then grouped and organised to create data clusters.

Different Unsupervised Learning Algorithms

Clustering Algorithm: The algorithm aims to identify similarities between various data items. Such as common size, shape, price or more. Finally, group these data to form meaningful clusters. 

Outlier Detection: Contrary to the Clustering algorithm, outlier detection is a method of finding dissimilarities or anomalies in the data. The most common use of the algorithm is in detecting any possible fraud. For instance, a credit card transaction way beyond the usual monthly expense would be detected as an anomaly, hence fraud.

Association Rule Mining: This mining method  searches for the most frequently occurring associations between multiple data. For instance, association defining the two actions happening together. Such as users buying health products are also subscribing for healthy meal plans.

Autoencoders: Using this mining method, the input dataset is further compressed into a coded form. Thus, recreating the data by removing any possible occurrence of noisy data. This technique is useful in improving the quality of videos and images.

Reinforcement Algorithm

Reinforcement learning works like feedback in a loop. Sensing the environment, the algorithm interacts with it through various actions. Simultaneously, observing the impact of the actions from the outside world. For each action, the algorithm receives a reward as feedback. 

The process continues over and over while the training model keeps exploring the best way to react. In plain human terms, the algorithm utilises trial and error methods to train AI models to be accurate. 

ChatGPT, the most popular AI chatbot uses a Reinforcement learning algorithm.

How Does Reinforcement Algorithm Work?

Exploration and exploitation are crucial aspects of reinforcement learning. Exploration refers to the agent’s strategy of trying out different actions to gather information about the environment and discover potentially better actions. Exploitation, on the other hand, involves using the knowledge gained so far to select actions that are believed to yield the highest rewards based on the agent’s current understanding of the environment.

The exploration-exploitation trade-off is essential because the agent needs to strike a balance between gathering new information (exploration) and exploiting the current knowledge to maximize rewards (exploitation). If the agent only focuses on exploitation, it may miss out on better actions that it hasn’t explored yet. Conversely, if the agent only explores and doesn’t exploit, it may not make the most of the knowledge it has gained.

One common approach to balancing exploration and exploitation is through the use of exploration strategies, such as epsilon-greedy or Thompson sampling. These strategies allow the agent to randomly or selectively explore actions with uncertain outcomes while also exploiting actions that have been found to be rewarding in the past.

By continuously interacting with the environment, learning from rewards, and adjusting its behaviour based on the observed outcomes, the agent aims to improve its decision-making abilities and achieve the best possible long-term reward

Machine Learning Algorithms – Supervised vs Unsupervised vs Reinforcement Learning

While all these algorithms aim at making AI models accurate in their responses, they are different from one another in many ways. 

Training Dataset

Supervised algorithm works with a labeled dataset to become accurate overtime. Unlike the prior one, the unsupervised algorithm identifies unknown patterns from the unlabeled dataset. 

Reinforcement algorithms, on the other hand, do not require any dataset for training. Instead, it learns through sensing its environment and interacting with it through various actions.

Learnings Methods

Supervised learning requires supervision, as the name suggests. It has examples at its disposal in the form of labeled datasets. Unsupervised algorithms utilising the power of reasoning and works without assistance. 

The reinforcement algorithm, as discussed, trains itself through experiences. The learning algorithm explores and exploits various actions, taken over time, to maximise long-term rewards.

Algorithm Goals

Depending on the provided labeled training dataset, supervised algorithms aim at mapping the input data to the respective output data. In contrast, the unsupervised algorithm’s objective is to identify patterns hidden within the unlabeled dataset. 

Reinforcement learning, on the other hand, is goal-oriented. It aims at maximising future rewards through taking a sequence of actions by exploration and exploitation of the environment. 

Training Medium (Offline vs Online)

Supervised algorithm undergoes offline training. However, training of Unsupervised and Reinforcement algorithms happen in real-time and online.

Al Applications

Supervised algorithms help predict profits, sales and more. While Unsupervised algorithm performs data association and clustering. It also allows for fraud detection, network security analysis and more.

Reinforcement learning algorithms are used for training self-driving cars, healthcare equipment, robots and more. ChatGPT is a popular example of a content creation tool that utilises reinforcement learning for training.

Criteria	Supervised Algorithms	Unsupervised Algorithms	Reinforcement Algorithms
Training Dataset	Labeled Dataset	Unlabeled Dataset	No pre-defined dataset required
Learnings Methods	Learns with examples	Learns by identifying hidden patterns	Leans through experiences and exploring & exploiting different actions
Algorithm Goals	To predict meaningful output	To identify data association and clusters	To maximize the future rewards through sequential actions
Training	Offline	Online	Online
Al Applications	Risk evaluation, price prediction etc	Fraud detection, data classifications	Self driving car, robots, medical equipment

Conclusion

Machine learning algorithms are categorised into three broad categories: Supervised, Unsupervised, and Reinforcement Learning tasks.

While Supervised algorithms work with labeled data, unsupervised learning utilises unlabeled data to find hidden patterns. Contrary to both, the Reinforcement learning requires no predefined data set and interacts with the environment to predict actions through trial and error method. 

While Supervised learning models tend to be very accurate, training these models involve significant expertise and time. Additionally, if the data changes, these models may need to be rebuilt. Tasks like regression and classification are examples of supervised learning.

On the other hand, Unsupervised learning occurs without the guidance of a supervisor. The input data given to machine learning algorithms is unlabeled, meaning there is no known output for each input. The algorithm itself discovers patterns and trends in the input data and establishes associations between different attributes.

The most efficient of all is reinforcement learning. As it learns through trial and error requiring least supervision. While all these machine learning algorithms have their own importance, these still are in their nascent phase. And might require a few more years to go beyond the various concerns that surround the technology today.