4 Kinds Of Machine Learning Algorithms


How some of us view machine learning might still be out-dated. Images of science fiction films pop into our head but in reality, they have become a widely used business tool that increases many operations within a business. If you want to keep up with your competitors you may need to start using algorithms.

When beginning to use algorithms in you are going to need a lot of additional resources. The first step is to fully understand how your ML is going to be used for your business. Each different algorithm will have its own advantages.

We are going to go over four of the most significant kinds of machine learning algorithms, discuss how each is used and what benefits you could gain from them.

Supervised Machine Learning Algorithms

The title will provide a big clue, but this type involves the supervision of operation. The developer has labeled sample data corpus and there are strict boundaries in which the algorithm must operate. This can be looked at as spoonfed machine learning:

  • You need to choose the information output you want to feed the algorithm
  • You need to choose the type of results you wish to gain (e.g. yes/no or true/false)

The machine treats this process similar to a ‘connect the dots’.

The main use of supervised machine learning is to make predictions of futures data, whether it is unseen or not available. The results are derived from labeled sample data. It involves 2 main processes: classification and regression.

Classification: Incoming data is classified based on data samples from the past. It is a manual process that teaches the algorithm to notice specific kinds of objects and put them into specific categories. The system must be able to recognize various kinds of information and carry out an optical character, image or binary recognition. Data can be compliant or non-compliant to certain requisites in the form of yes or no.

Regression: Patterns are recognized and predictions of continuous outcomes are calculated. It is necessary for the system to comprehend numbers and their values, heights widths, etc.

Below is a list of the most commonly used supervised algorithms:

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

Cases of Supervised Learning Algorithms

These types of algorithms are often used to determine future prices and forecast patterns in sales, retail and stock trades. The algorithm uses incoming data to analyze and calculate the possibility of certain outcomes. Seismic and Highspot are sales enabled platforms that have these algorithms in place.

It is also common to see these algorithms used in the world of advertising. They can help ascertain the possible price of ad spaces, helping companies keep within their budget.

Unsupervised Machine Learning Algorithms

In the case of unsupervised learning, the developer doesn’t have direct control. Supervised learning is used for when you already have the results you want them to be classified. The results are not known in unsupervised learning and the still need to bee defined.

The other difference you will notice is that supervised learning requires only labelled data, whereas unsupervised learning uses unlabelled data.

The following is a list of when we would use unsupervised learning:

  • Investigating how information is structured
  • Taking valuable insights
  • Spotting patterns
  • Putting this into practice to improve efficiency

To put it simply, unsupervised learning describes information. It sorts through it so that others can understand it using two techniques:

Clustering: Exploring data to put it into clusters or groups. They are grouped depending on their internal patterns and there isn’t any previous knowledge of the group’s qualifications. These qualifications are based on how individual data objects are similar and in some cases different from the rest.

Dimensionality reduction: Algorithms use dimensionality reduction to get rid of the incoming noise to ‘settle’ the information that is relevant.

The most commonly used algorithms are:

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

Cases of Unsupervised Learning Algorithms:

These algorithms have the most impact in the digital marketing industry. Aside from that, it is often found being used to adjust services depending on customer information that has been collected.

The problem here is that we rely on ‘known unknowns’. If a business is unable to clarify the unlabelled data and take out what is relevant, the business operations aren’t going to be as effective. Lotame and Salesforce have learned have to use these types of ML algorithms to provide cutting edge data management platforms.

Behavioral data, personal data, and certain software are some of the qualifications in which unsupervised learning can effectively identify target audiences.  It is great to help companies develop ad campaigns and recognize patterns.

Semi-supervised Machine Learning

The good news is the names of machine learning algorithms are very straight forward! Semi-supervised machine learning is half-way between supervised and unsupervised and involves a little of each.

This is how they work:

  • Semi-supervised machine learning uses a certain amount of labeled data in order to teach itself.
  • The partially taught model can now label the unlabelled data. As there are some restrictions on the sample data, the results are known as pseudo-labeled data.
  • The labeled and pseudo–labeled data are now combined to form a new algorithm. This combination has parts that are descriptive and others that are predictive.

Semi-supervised learning is about identifying data assets and grouping it into different parts.

Cases of Semi-supervised Machine Learning

The legal and healthcare industries are helped by semi-supervised learning, to name a couple. It can manage the web content classification as well as image and speech analysis.

In web content classification, semi-supervised learning is used for crawling engines and content aggregation systems. A large range of labels is used to analyze content and separate it into certain configurations. It is normal that human input is needed for further classification.

GATE and uClassify are perfect examples of semi-supervised learning. MRI and CT scans are examples of how algorithms perform labeling for image analysis. By comparing one set of scans to a set of perfect existing scans, anomalies can be spotted.

Reinforcement Machine Learning Algorithms

Many would understand this as a machine-learning artificial intelligence. The system developed is by a series of tries and fails which improves itself based on both the labeled data and the interactions with incoming data.

The main technique is exploration/exploitation. In theory, it is simple. When an action happens, the results are observed. The next action will depend on the results of the previous one.

Within reinforcement machine learning there are reward signals. It is similar to training a puppy. Reward signals (or treats) are provided as a way of learning which actions are right or wrong. Positive reward signals are used to reinforce the right course of action. Negative reward signals encourage the correction of incorrect actions.

Reward signals can be classified further depending on the nature of the information. Here are the most common reinforcement algorithms:

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

Cases of Reinforced Machine Learning Algorithms

Modern NPCs and other videos are great examples. The AI reacts depending on the actions of the player, because of this, the AI player is always coming up with new challenges.

Reinforced learning algorithms are needed to self-driving cars. A car will detect a left turn and activate the necessary actions. It can also be used in the Marketing and Ad industry for retargeting operations.

When helping natural language processes (NPL) reinforced learning can encourage dialogue generation for chatbots to:

  • Copy the style of input messages
  • Create knowledgable responses
  • Discover relevant responses based on user interaction

What are the overall thoughts of ML?

There’s no doubt that machine learning algorithms can play a huge part in any number of industries. Thanks to algorithms, problems can be solved and valuable insights can be taken.