Machine Learning Algorithms: 4 Types You Should Know

Machine Learning came a long way from a science fiction fancy to a reliable and diverse business tool amplifying multiple elements of the business operation. In fact, its influence on business performance may be so big that these days implementation of Machine Learning algorithms is necessary to maintain competitiveness in the modern environment regardless of the field of activity.

However, while Machine Learning buzz is all-time high and its adoption rate is steadily growing (add relevant stats) — overall awareness of whats, whys, and hows of the technology is still relatively lacking.

Given the fact, that implementation of Machine Learning into a business operation is a strategic step — it makes sense to understand what kind of algorithms this or that operation may require.

In this article, we will break down the major types of machine learning algorithms and explain the purpose of each of them.

Types of Machine Learning Algorithms

Supervised Machine 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.

Basically, 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 main purpose of supervised learning is:

• scale the scope of data;
• to make predictions of unavailable, future or simply unseen data based on a labeled sample data.

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

• Classification means the system capacity to differentiate types of information, perform optical character or image recognition, and also binary recognition (whether a particular bit of data is compliant or non-compliant to certain requirements in a manner of “yes” or “no”). Classification labels the incoming data based on past data samples. This process manually trains the algorithm to recognize certain types of objects and categorize them accordingly.
• Regression AKA (prediction of continuous outcomes) means the machine’s understanding of numbers, its values, grouping (for example, height and width) and outcomes. This process is used to identify patterns and calculate predictions out of it.

The most widely used algorithms are:

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

Use Cases

The most common fields of use for supervised learning are 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.

Supervised learning is also used in ad tech operations as part of the ad content delivery sequence. The role of a supervised learning algorithm 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 of time).

Unsupervised Machine Learning Algorithms

Unsupervised Learning is the one that does not involve direct control of the developer. If the key point of supervised machine learning is that you know the results and need to sort out the data, then in case of unsupervised machine learning algorithms 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.

This is the key thing to understand about it. The unsupervised machine learning algorithm is used to:

1. explore the structure of the information;
2. extract valuable insights;
3. detect patterns;
4. implement this into its operation in order to increase efficiency.

In other words, it describes information — sifts through it and makes sense of it.

In order to make that happen, unsupervised learning applies the following techniques:

• Clustering — 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 similarity of certain data objects and also aspects of its dissimilarity from the rest (which can also be used to detect anomalies).
• Dimensionality reduction. Incoming data is often riddled with noise — pieces of data that present little to no interest in the requirement of a specific operation. Dimensionality reduction is used to remove this noise while distilling the relevant information.

The most widely used algorithms are:

• k-means clustering,
• t-SNE;
• PCA;
• Association rule.

Use Cases

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

In one way or another, modern data management platforms are equipped with unsupervised algorithms for various operations. Probably the most cutting-edge options are now provided by Lotame and Salesforce.

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 certain elements of both into a thing of its own.

Here’s how semi-supervised algorithms work:

• A semi-supervised machine-learning algorithm uses a limited set of labeled sample data in order to shape the requirements of the operation (i.e. train itself).
• The limitation results in a partially trained model that is subsequently tasked with labeling unlabeled data.
• However, due to the limitations of the sample data set, the results are considered pseudo-labeled data.
• 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.

Technique-wise, semi-supervised learning applies the Classification process to identify data assets and the Clustering process to group it into distinct parts.

Use Cases

These days, semi-supervised learning is widely used in web content classification, image and speech analysis in legal and healthcare fields.

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 certain configurations. However, this procedure usually requires human input in order to further the classification.

A good example of this will be uClassify. The other well-known tool of this category is 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 represents what is commonly understood as machine learning artificial intelligence.

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

This sequence can be referred to as an exploration/exploitation technique. Basically, it looks like this: an action is taken — results are observed and implemented in the next action.

In the center of reinforcement learning algorithms are reward signals that occur upon performing certain 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 courses of action.

There are two major types of reward signals:

• Positive reward signal encourages continuing performance a certain sequence of action
• Negative reward signal penalizes for performing certain actions and urges to course correct in order 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;
• Monte-Carlo Tree Search (MCTS);
• Asynchronous Actor-Critic Agents (A3C);
• Temporal Difference (TD).

Use cases

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 information and relevant operating procedures.

Most commonly, this type of machine learning is used in video games (the majority of modern enemy NPCs are acting based on this model). Reinforcement Learning provides flexibility to the AI reactions to the player’s action thus providing viable challenges. For example, it is used for collision detection of the moving vehicle and passerby in the Grand Theft Auto series.

Also — reinforcement learning is used for self-driving cars. For example, if the self-driving car (Waymo, for example) 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 the current board position.

On the other hand, Reinforcement Learning is commonly used in online marketing and ad tech in particular. The thing is — Reinforcement learning 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 and dialogue generation for chatbots. It can be implemented for various purposes, for example:

• 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 developing such bot became more of a UX challenge than a technical feat.

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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.