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A Guide To The Types Of Machine Learning Algorithms

Data is the vital spark of all businesses. In today’s open market, data-directed actions make a significant difference between beating the competition and lagging. That is why machine learning can grow to become the key to unleashing the potential of data and adopting practices that let a company lead the pack.

Machine learning is an intricate subject area that has much in common with other related fields, including artificial intelligence. Overall, there are various types of ML algorithms that you may bump into on the way to data awareness: from comprehensive methodologies to narrow-context techniques to machine tending robots.

In this post, we’ll provide a gentle introduction to the major types of ML algorithms and the benefits they entail for your business.

Types of ML Algorithms

Supervised Machine Learning

Supervised machine learning is a branch of ML that combines algorithms and methods of the model building based on a set of examples containing “known input – known output” pairs. In this case, we teach or train the machine using data that are properly or correctly labeled. 

This is the most common one among other machine learning algorithm types since it is easier to train a machine with selected data. Depending on what you want to predict, supervised machine learning can be used to solve two types of problems: a regression problem and a classification problem.

At present, a large number of supervised ML algorithms have been developed, each of them having its own strengths and weaknesses. There is no single algorithm that is best suited to all analysis tasks.

The most widespread supervised algorithms are:

  • decision trees;
  • support vector machines;
  • Bayesian classifier;
  • linear discriminant analysis;
  • k-nearest neighbor method;
  • linear regression;
  • logistic regression;
  • neural networks.

The main goal of supervised learning is to classify and process data using machine language. Within this type, a classified data set is used to infer a learning algorithm. This data then helps predict the classification of other unlabeled or unseen data.

Supervised Learning Algorithms Use Cases

The most popular use cases for supervised learning include price forecasting prediction and determining possible trends in sales, retail marketing, and stock trading. In all cases, an algorithm uses input data to realize the potential and figure potential results.

Supervised machine learning can also be used to assess the financial risk of individuals and organizations on the basis of available information about the past financial activity. Moreover, supervised learning allows for good predictions about purchasing behavior based on previous patterns.

From the business standpoint, this ML type is also applicable to ad tech operations within the frame of ad content delivery sequence. The focal area of supervised learning is the evaluation of prices for ad spaces and their value during the live bidding process. 

Unsupervised Machine Learning Algorithms

Unlike supervised learning, this ML type does not need labels and corresponding outputs to be provided. Instead, unsupervised learning uses unlabeled input data and determines the structure of the set. Therefore, it’s perfect when there are no correct answers, but only objects and their features. Unsupervised learning is typically used for clustering, anomaly detection, association mining, and dimensionality reduction. Let’s have a closer look at some of these algorithms:

  • Clustering – if you have a large amount of data, the most effective way to figure out how to handle it is to sort the unclassified data into groups for the primary analysis. Essentially, clustering is a multivariate statistical procedure that collects data containing information about a sample of objects and then arranges the objects into relatively homogeneous groups. Clustering can be used to divide users of an online store into segments to facilitate recommendation patterns.
  • Dimensionality reduction – refers to reducing the number of features in the dataset. The presence of redundant, uninformative, or weakly informative features can reduce the effectiveness of the model. Dimensionality reduction helps to reduce the size of the dataset so that the work of ML algorithms on it is accelerated. 

The frequently-used unsupervised algorithms are:

  • k-means clustering
  • Association rule
  • Principal Component Analysis
  • t-Distributed Stochastic Neighbor Embedding
  • Usage Scenarios of Unsupervised Learning Algorithms

Online marketing and ad tech are the areas where this type of ML has found wide application. It helps to make the most of the data, content, and online channels to boost productivity and better understand the target audience. On this note, unsupervised learning can be used to define target audience groups according to specific criteria. As a result, companies obtain more effective targeting of ad content. 

Moreover, unsupervised learning reinforces effective data management. Today, Lotame, Master of Code Global and Salesforce are the world’s leading data solutions companies that leverage machine learning algorithms.


Semi-supervised Machine Learning Algorithms

Semi-supervised learning is an ML technique that uses both labeled and unlabeled data for training. This type is a compromise between unsupervised (without any marked-up training data) and supervised learning (with a fully marked-up training set). The primary purpose of this type is to improve classification performance since labeled data can be expensive, whereas unlabeled data can be plentiful.

Applications of Semi-supervised Machine Learning

This type of machine learning is widely applied to internet content classification. Since web pages are plentiful, it’s almost impossible to label all the web pages. And semi-supervised machine learning is what helps to classify web pages.

Among other things, this kind of ML learning allows us to analyze audio and video content. As with web pages, we have an ocean of media that is unfeasible to label. Again, semi-supervised learning comes in handy there as well.

The method can be used to recognize impersonation frauds. Scams can be classified as an anomaly in the background of usual activity. Semi-supervised learning allows creating models that recognize such anomalies. Those systems are often used to detect fraud attempts in online transactions.

This type of ML learning can also be applied in cases where there are ready sets of partially marked-up data, which is common in large enterprises. Amazon, for example, has improved the ability of its Alexa digital assistant to understand natural language by training artificial intelligence algorithms on combinations of marked and unmarked data. This has improved the accuracy of Alexa’s responses.

Reinforcement Machine Learning Algorithms

Reinforcement learning is a type of ML algorithm which lets software agents and machines automatically identify the suitable behavior within a particular situation, to increase its performance. It also provides a way to overcome the limitations of deep learning to address a multi-step problem.

The focus of reinforcement learning is on regimented learning processes when the machine learning algorithm is provided with a set of actions, parameters, and final values. Once the rules are defined, the machine learning algorithm attempts to explore different options and possibilities, tracking and evaluating the outcome each time to determine which option is optimal. Thus, reinforcement learning solves problems by trial and error. It learns from past experiences and changes its approach in response to a new situation, trying to achieve the best possible outcome.

The machine’s goal is validated in the form of a special signal called a reward. These signals are granted to the machine each time when it completes a task correctly. By automating the calculation of rewards, you can allow the machine to learn on its own.

Most popular reinforced algorithms include:

  • Q-Learning
  • Temporal Difference 
  • Monte-Carlo Tree Search
  • Asynchronous Actor-Critic Agents

Applications of Reinforced Learning In Real World

The first use case that immediately comes to mind is reinforcement learning for autonomous driving. In self-driving cars, there is a gamut of factors to take note of starting from speed limits at different locations to avoiding collisions.

Therefore, a great number of self-driving activities get facilitated with the help of reinforced learning. These include trajectory optimization, scenario-based learning policies for highways, dynamic pathing, controller optimization, and others.

RL is also indispensable when it comes to creating next-gen video games and upending traditional methods in game creation. In particular, reinforced learning has proven to be effective in FPS games.

Another daily application of reinforcement learning is sorting items in retail stores. Some retailers are experimenting with robotic systems for sorting clothing, shoes, and accessories. By a combination of RL and deep learning, robots determine how hard to squeeze an item and which grip is best.

Healthcare has also benefited from this cutting-edge technology. Patients can get medical treatment from policies obtained from RL systems. Reinforced learning is able to identify the best possible policies using previous experiences. It makes this method more convenient than other control-based systems in the medicine industry. 

Other applications of reinforced learning include Natural Language Processing, industry automation, new recommendations, engineering – the list goes on.

In general, reinforced deep learning is well suited for autonomous decision-making in cases where supervised and unsupervised learning opportunities are insufficient.

The Bottom Line

Choosing the right machine learning algorithm depends on several factors. These may include the size of the data as well as its quality and variety. Also, you need a deep understanding of answers your business needs based on that data. You also have to pay attention to accuracy, training time, parameters, data, and more. Therefore, picking the right algorithm is more about a combination of business needs, specifications, experimental work, and time available.