4 Types of Machine Learning Algorithms

Machine Learning came a long way from a science fiction fancy to a reliable and diverse business tool that amplifies multiple elements of the business operation.

Its influence on business performance may be so significant that the implementation of machine learning algorithms is required to maintain competitiveness in many fields and industries.

The implementation of machine learning in business operations is a strategic step and requires a lot of resources. Therefore, it’s important to understand what do you want the ML to do for your particular business and what kind of perks different types of ML algorithms bring to the table. 

In this article, we’ll cover the major types of machine learning algorithms, explain the purpose of each of them, and see what the benefits are.

Types of Machine Learning Algorithms

Algorithms include supervised and unsupervised learning systems as well as Reinforcement and Semi-supervised machine learning technology. 

Supervised machine learning algorithms

Supervised Learning Algorithms

Supervised Learning Algorithms are the ones that involve direct supervision (cue the title) of the operation. In this case, the developer labels sample data corpus and set strict boundaries upon which the algorithm operates.

It is a spoonfed version of machine learning:

  • you select what kind of information output (samples) to “feed” the algorithm;
  • what kind of results it is desired (for example “yes/no” or “true/false”).

From the machine’s point of view, this process becomes more or less a “connect the dots” routine.

The primary purpose of supervised learning is to scale the scope of input data and to make predictions of unavailable, future or unseen data based on labeled sample data.


Supervised machine learning includes two major processes: classification and regression.

  • Classification is the process where incoming data is labeled based on past data samples and manually trains the algorithm to recognize certain types of objects and categorize them accordingly. The system has to know how to differentiate types of information, perform an optical character, image, or binary recognition (whether a particular bit of data is compliant or non-compliant to specific requirements in a manner of “yes” or “no”).
  • Regression is the process of identifying patterns and calculating the predictions of continuous outcomes. The system has to understand the numbers, their values, grouping (for example, heights and widths), etc. 

The most widely used supervised algorithms are:

  • Linear Regression
  • Logistical Regression
  • Random Forest
  • Gradient Boosted Trees
  • Support Vector Machines (SVM)
  • Neural Networks
  • Decision Trees
  • Naive Bayes
  • Nearest Neighbor

Supervised Learning Algorithms Use Cases

The most common fields of use for supervised learning algorithm is price prediction and trend forecasting in sales, retail commerce, and stock trading. In both cases, an algorithm uses incoming data to assess the possibility and calculate possible outcomes.

The best examples are Sales enablement platforms like Seismic and Highspot use this kind of an algorithm to present various possible scenarios for consideration.

Business cases for supervised learning method include ad tech operations as part of the ad content delivery sequence. The role of the supervised learning system there is to assess possible prices of ad spaces and its value during the real-time bidding process and also keep the budget spending under specific limitations (for example, the price range of a single buy and overall budget for a certain period).


Unsupervised Machine Learning Algorithms

Unsupervised Learning Algorithms

Unsupervised Learning is one that does not involve direct control of the developer. If the main point of supervised machine learning is that you know the results and need to sort out the new data, then in the case of unsupervised learning algorithm the desired results are unknown and yet to be defined.

Another big difference between the two is that supervised learning uses labeled data exclusively, while unsupervised learning feeds on unlabeled data.

The unsupervised machine learning algorithm is used for:

  • exploring the structure of the information;
  • extracting valuable insights;
  • detecting patterns;
  • implementing this into its operation to increase efficiency.

In other words, unsupervised learning techniques describe information by sifting through it and making sense of it.


Unsupervised learning algorithms apply the following techniques to describe the data:

  • Clustering: it is an exploration of data used to segment it into meaningful groups (i.e., clusters) based on their internal patterns without prior knowledge of group credentials. The credentials are defined by the similarity of individual data objects and also aspects of their dissimilarity from the rest (which can also be used to detect anomalies).
  • Dimensionality reduction: there is a lot of noise in the incoming data. Machine learning algorithms use dimensionality reduction to remove this noise while distilling the relevant information.

The most widely used algorithms are:

  • k-means clustering
  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • PCA (Principal Component Analysis)
  • Association rule


Use Cases of Unsupervised Learning Algorithms

Digital marketing and ad tech are the fields where unsupervised learning is used to its maximum effect. In addition to that, this algorithm is often applied to explore customer information and adjust the service accordingly.

The thing is – there are a lot of so-called “known unknowns” in the incoming data. The very effectiveness of the business operation depends on the ability to make sense of unlabeled data and extract relevant insights out of it.


Unsupervised algorithms equip modern data management. At the moment, Lotame and Salesforce are among the most cutting-edge data management platforms that implement this machine learning system.

As such, unsupervised learning can be used to identify target audience groups based on certain credentials (it can be behavioral data, elements of personal data, specific software setting or else). This algorithm can be used to develop more efficient targeting of ad content and also for identifying patterns in the campaign performance.


Semi-supervised Machine Learning Algorithms

Semi-supervised Machine Learning Algorithms

Semi-supervised learning algorithms represent a middle ground between supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a thing of its own.


Here’s how semi-supervised algorithms work:

  1. A semi-supervised machine-learning algorithm uses a limited set of labeled sample data to shape the requirements of the operation (i.e., train itself).
  2. The limitation results in a partially trained model that later gets the task to label the unlabeled data. Due to the limitations of the sample data set, the results are considered pseudo-labeled data.
  3. Finally, labeled and pseudo-labeled data sets are combined, which creates a distinct algorithm that combines descriptive and predictive aspects of supervised and unsupervised learning.

Semi-supervised learning uses the classification process to identify data assets and the clustering process to group it into distinct parts.


Semi-supervised Machine Learning Use Cases

Legal and Healthcare industries, among others, manage web content classification, image, and speech analysis with the help of semi-supervised learning.

In the case of web content classification, semi-supervised learning is applied for crawling engines and content aggregation systems. In both cases, it uses a wide array of labels to analyze content and arrange it in specific configurations. However, this procedure usually requires human input for further classification.

An excellent example of this will be uClassify. The other well-known tool of this category is the GATE (General Architecture for Text Engineering).

In the case of image and speech analysis, an algorithm performs labeling to provide a viable image or speech analytic model with coherent transcription based on a sample corpus. For example, it can be an MRI or CT scan. With a small set of exemplary scans, it is possible to provide a coherent model able to identify anomalies in the images.


Reinforcement Machine Learning Algorithms

Reinforcement Learning Algorithms

Reinforcement learning represents what is commonly understood as machine learning artificial intelligence.

In essence, reinforcement learning is all about developing a self-sustained system that, throughout contiguous sequences of tries and fails, improves itself based on the combination of labeled data and interactions with the incoming data.

Reinforced ML uses the technique called exploration/exploitation. The mechanics are simple – the action takes place, the consequences are observed, and the next action considers the results of the first action.

In the center of reinforcement learning algorithms are reward signals that occur upon performing specific tasks. In a way, reward signals are serving as a navigation tool for the reinforcement algorithms. They give it an understanding of right and wrong course of action.


Two main types of reward signals are:

  • Positive reward signal encourages continuing performance a particular sequence of action
  • Negative reward signal penalizes for performing certain activities and urges to correct the algorithm to stop getting penalties.

However, the function of the reward signal may vary depending on the nature of the information. Thus reward signals may be further classified depending on the requirements of the operation. Overall, the system tries to maximize positive rewards and minimize the negatives.

Most common reinforcement learning algorithms include:

  • Q-Learning
  • Temporal Difference (TD)
  • Monte-Carlo Tree Search (MCTS)
  • Asynchronous Actor-Critic Agents (A3C)

Use Cases for Reinforced Machine Learning Algorithms

Reinforcement Machine Learning fits for instances of limited or inconsistent information available. In this case, an algorithm can form its operating procedures based on interactions with data and relevant processes.

Modern NPCs and other video games use this type of machine learning model a lot. Reinforcement Learning provides flexibility to the AI reactions to the player’s action thus providing viable challenges. For example, the collision detection feature uses this type of ML algorithm for the moving vehicles and people in the Grand Theft Auto series.

How to make your IT project secured?

Download Secure Coding Guide

Self-driving cars also rely on reinforced learning algorithms as well. For example, if the self-driving car (Waymo, for instance) detects the road turn to the left – it may activate the “turn left” scenario and so on.

The most famous example of this variation of reinforcement learning is AlphaGo that went head to head with the second-best Go player in the world and outplayed him by calculating the sequences of actions out of current board position.

On the other hand, Marketing and Ad Tech operations also use Reinforcement Learning. This type of machine learning algorithm can make retargeting operation much more flexible and efficient in delivering conversion by closely adapting to the user’s behavior and surrounding context.


Also, Reinforcement learning is used to amplify and adjust natural language processing (NLP) and dialogue generation for chatbots to:

  • mimic the style of an input message
  • develop more engaging, informative kinds of responses
  • find relevant responses according to the user reaction.

With the emergence of Google DialogFlow building, such bot became more of a UX challenge than a technical feat.

What do we think about ML intelligent algorithm?

As you can see, different types of machine learning algorithms are solving different kinds of problems. The combination of different algorithms makes a power capable of handling a wide variety of tasks and extracting valuable insights out of all sorts of information.

Whether your business is a taxi app or a food delivery service or even a social media network – every app can benefit from machine learning algorithms. Ready to begin? The APP Solutions team has expertise in architecting and implementing ML algorithms into various types of projects and we’d love to see your business grow.

Looking for machine learning developers?

Let's discuss details

Valohai: Effective Management for Machine Learning Workflow

Created in conjunction with the Valohai team.

Machine Learning is a trend that’s here to stay. The survey of people in North America, Europe, and APAC shows that 28% think the ML trend will significantly impact society as a whole. Regarding personal impact, only 24% of respondents don’t think machine learning will have any influence on their life. (Granted, those people probably don’t realize that ML already affects their life whenever they use Google, Uber, or watch yet another show on Netflix.)

Reasons for machine learning adoption in organizations as of 2018

[Source: Statista]

However, while machine learning is something that can be applied to each industry (given the desire and resources), it is not as straightforward as we’d like. Can something be done about it? For sure.

What’s the problem with the machine learning workflow?

The problem with production-level machine learning (at the current stage as it is) is three-fold:

  1. Setting up and maintaining ML Infrastructure requires a lot of manual work
  2. There aren’t any universal standards
  3. Machine learning experiments lack version-control

Too much manual work

One example of the abovementioned manual work is related to machine orchestration. Currently, data scientists need SSH for a server, install the latest Nvidia drivers, Python dependencies, as well as clusters of 100 GPUs hosting Docker containers to get the training running. These and many other steps in the machine learning pipeline from feature extraction to training and deployment result in so-called glue code.

When the time comes for machine learning code itself, it takes up to 5% of the project. When you have separate packages for various features and functionalities like data analysis and verification, configuration, infrastructure, etc., a lot of manual work is required to get the data you need from all these sources.

No universal standards

The other challenge is that everyone’s got their own standards when it comes to machine learning. It might be personal preferences on how to keep records of hyperparameters (in case you’re working with ML algorithms yourself), or it can be corporate standards about the tooling.

However, the issue remains – the use of various technologies and non-integrated teams create havoc on the global market. For example, the IT department is responsible for one part of the pipeline, Data Engineers are working on another piece, while Data Scientists are trying to manage the entire thing while none of them have a clear view on what the others are doing and with what technology. This leads to a situation where Data Scientists need to relearn everything every time they switch projects.

No version-control

Finally, there’s the issue with version control that is a ground-rule in software development but not yet in machine learning development. When data scientists run an ML experiment, usually they just store the training code (the input) and the trained model (the output).

When it’s the scale of a presentation you’re working on with your teammates, you can easily find out who changed what. When it’s an ML algorithm that is being trained, the implications of the environmental impact, for example, can alter the training process in an unexpected way.

Version control should be present in every experiment to be able to reproduce the results or give an explanation of how the model was built.

About Valohai Deep Learning Management Platform

The APP Solutions team has learned about the Valohai product at the Web Summit 2018, and we immediately felt that this is something we’d like to learn more about.

Valohai is a deep learning management platform that automates deep learning infrastructure for data science teams.

The features include:

  • Zero setup infrastructure: depending on the nature of your project and preferences, you can train your models in the cloud or on your servers with the click of a button.
  • Built-in version control: reproduce any previous run with integrated version control for input data, hyperparameters, training algorithms, and environments.
  • Numerous integration options: the platform is tool-agnostic. Whether your project uses Excel, TensorFlow, Python, Darknet, GitHub, Docker, and any other tools for the machine learning models training, the Valohai DL management platform can be used with any runtime.
  • Pipeline automation: Valohai relies on API first development. Therefore you can easily integrate your machine learning pipeline into the existing development pipeline.
  • Standards, finally: the workflow is standardized in Valohai so that you benefit from the industry best practices that help such giants as Facebook, Netflix, and Uber work.
  • Monitoring and visualizations: visual feedback from the training’s performance and monitor all data in real-time.
  • Easy scalability: Run models on hundreds of CPUs and GPUs with a push of a button.t.
  • Deployment: Find the best models and deploy them to the production straight from Valohai
  • Teamwork: Have a transparent view of what experiments others are doing

Valohai Featured Cases

One of the prominent cases of Valohai deep learning management platform implementation was real-time detection of sexual abuse materials from Darknet. The sensitive training material is seized so even the machine learning developers can’t see the material themselves and the training environment is running as an air-gapped on-premise installation.

Due to the size of a typical data set for this particular project (around 30Gb on average or up to twice as much for the largest one), the standard ways of training the models were too cumbersome. With the introduction of Valohai’s platform, the machine learning models could be continuously retrained while the resources were efficiently used as they were needed. You can read more about this case here.

Another case was related to logistics – creating and speeding up the model training for a self-driving ferry prototype. The team of two, with the help of Valohai DLP, shortened the training time from 2 weeks to 3 days. Read more about this case here.

The APP Solutions Machine Learning Expertise

The APP Solutions provides not just standard web & mobile development, but also data analytics and machine learning projects.

Currently, we have experience in healthcare, communication, and user behavior projects that implement ML features. We also have projects in Ad Tech and Financial Services that require machine learning as well to detect and fight fraudulent activities, which is why we think the APP Solutions and Valohai partnership is a win-win situation for everyone involved.

Mykola Slobodian about Valohai Deep Learning Management Platform

“It was the answer to so many of our questions, especially the version control. After all, when you, for example, train a machine learning model to distinguish between diseases or DNA sequences, it’s important to monitor everything it’s learning from and make sure the models are easily scalable because the stakes in healthcare are incredibly high,” says Mykola Slobodian, CEO of the app development company APP Solutions.

Want to receive reading suggestions once a month?

Subscribe to our newsletters