Machine learning is one of our major specializations. We work with this technology and utilize various machine learning techniques every day. In this article, we want to show you some of the most popular and useful machine learning techniques, both supervised and unsupervised. It’s time to get acquainted with them!

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Machine learning is a technology that, after initial training, works without human assistance. It’s based on complex applications and algorithms that, for example:

  • Look for the perfect combination
  • Divide various elements into groups
  • Predict a particular value based on a set of prior data, and more

In general, machine learning techniques are divided into two major groups:

  1. Unsupervised machine learning techniques find hidden patterns or important structures in data. They are used to draw inferences from datasets consisting of input data without labeled responses. We do realize that this type of machine learning can be challenging to comprehend. To make it more transparent, let’s use a simple example: Let’s say there is an e-commerce business owner that wants to segment products with similar characteristics in their offer, but they do not want to indicate which characteristics should be taken into consideration. This is a situation when unsupervised machine learning should be used.
  2. Supervised machine learning techniques are much simpler. They are used every time you want to predict or explain the data you possess.

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Now, we will analyze the most common machine learning techniques. We start with unsupervised machine learning techniques.

Unsupervised machine learning techniques

CLUSTERING

Clustering is, just like in our example, all about grouping or clustering elements that have similar characteristics. And like in our e-commerce example, it’s the algorithm itself that defines which characteristics should be taken into account and what the outcome will be. The only thing that the data scientist can do is to verify the quality of the outcome.

The most popular clustering method is the K-Means method, where “K” represents the number of clusters that the data scientist chooses to devise. K-means clustering is all about dividing N observations into K clusters in which each observation belongs to the cluster with the nearest mean.

Machine Learning Techniques data scientist

DIMENSIONALITY REDUCTION

The second major unsupervised machine learning technique is called dimensionality reduction. It is the process of reducing the number of random variables taken into consideration by the ML algorithm by obtaining a set of principal variables. In other words, dimensionality reduction is used to remove the least important information (sometimes you have to deal with redundant columns, rows, etc.) from a dataset. Dimensionality reduction comprises feature selection and feature extraction.

In fact, this machine learning technique is essential when you’re working with large datasets. You see, the higher is the number of features or other pieces of data you have, the more difficult it is to work on your project. In such a situation, data scientists try to remove irrelevant or redundant information from a dataset, to make their work quicker and more straightforward.

data science charts

Supervised machine learning techniques

REGRESSION

There are five crucial types of regression:

  • Simple linear regression
  • Polynomial regression
  • Support vector regression
  • Decision tree regression
  • Random forest regression

In general, they help to predict (or to explain) a particular value based on a set of prior data. The most straightforward technique is simple linear regression. The other four are considered more complex. For instance, the technique called the decision tree helps in making decisions and is commonly used in operations research, business intelligence, and strategic planning.

You can use regression to predict such matters as an employee’s salary, disease spread, or property value. Actually, the decision tree technique is prevalent in real-life conditions. Every single time you think about whether you should go to a restaurant or order food–you use the decision tree technique.

decision tree machine learning techniques

CLASSIFICATION

This is yet another machine learning technique. Classification helps predict or explain a class value. They estimate the probability of an occurrence of an event based on one or more inputs. It’s the classification model that helps divide e-mails into spam and non-spam, assess whether a given image contains a dog or a cat, and finally, predict whether a given customer will buy a product (based on their behavior on the website and historical data). In most instances, the classification output can be ascribed to two (yes, no) or three (dog, cat, none) classes.

Classification can also be used to estimate whether the company will win the contract. Because the estimate is based on a probability, the output is a number (between 0 and 1), where 1 represents the answer yes, and 0 the answer no. If the probability is higher than 0.5, then we predict that, most likely, the company will win the contract. If the probability is less than 0.5, we can say that the company will probably not win this contract.

Now, it’s essential to answer the question of what’s the difference between regression and classification?

  • Classification is the task of predicting a discrete class label.
  • Regression always predicts a quantity.

Classification

ENSEMBLE METHODS

Ensemble methods combine several predictive models in order to receive high-quality predictions. The ensemble methods are used to reduce the variance and bias that comes with a single machine learning technique. The entire idea behind ensemble methods is based on the fact that a single model may be accurate under certain conditions but inaccurate under others. Thanks to combining two or more models, the quality of the outcome increases.

Ensemble methods use the same idea of combining several predictive models to get more high-quality predictions. For example, the random forest is an ensemble method that combines many decision trees. The random forest technique is far more accurate than just one decision tree.

predictions on business charts

Other machine learning techniques

So far, we have examined the most popular machine learning techniques. There are many more techniques, but their usefulness is strictly limited to specific machine learning projects and algorithms. Now, we want to analyze two other techniques, which are based on machine learning, although they cannot be strictly called pure machine learning techniques, namely:

  • Deep learning
  • Reinforcement learning

Both of these technologies are incredibly complex and advanced. You can think of them as top-level machine learning. Let’s take a closer look at them:

DEEP LEARNING

Deep learning is based on neural networks, which are designed to imitate the way the human brain works. The main objective of deep learning is to capture and understand non-linear patterns in data. It happens by adding additional layers of parameters to the model (making it deep). In other words, deep learning consists of more layers than typical machine learning algorithms, which permits higher levels of abstraction and, as a result, improved predictions.

deep learning

Of course, such a high level of accuracy comes with a price. The deep learning techniques require a lot of data and a lot of computing power. That’s why, in order to start and run the deep learning algorithms, you need very powerful computers with GPUs (Graphical Processing Units).

Today, deep learning is used in many significant fields, such as:

  • Self-driving cars
  • Fraud news detection
  • NLP (Natural Language Processing)
  • Virtual Assistants
  • Visual and face Recognition
  • Fraud Detection
  • Automatic Machine Translation

We can expect that, in the near future, deep learning will have many more interesting applications. It’s one of the technologies to watch in 2021!

REINFORCEMENT LEARNING

It’s another example of exciting ML-related technology. Reinforcement learning makes a case for itself when you have little or no historical data. Unlike traditional machine learning algorithms, the RL algorithms don’t need any information in advance. They learn from data during the process. We named this technology at the end because it’s a combination of traditional machine learning techniques and deep learning. Today, RL is utilized predominantly in robotics and industrial automation.

If you are interested in machine learning–drop us a line. Addepto is an experienced AI consulting company with strong machine learning background. We will gladly show you which machine learning techniques should be utilized in your project or company and show you the potential benefits that come from using artificial intelligence. We are at your service!

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