The entire idea behind machine learning (ML) is to go from data to insight. From a given problem (by large business one) to an adequate solution. The machine learning algorithms help in predicting future trends, changes, and opportunities. However, large datasets are essential in this task. To harness them, data scientists use several machine learning techniques and methods. In this article, you will find out what are the machine learning techniques and methods. We will analyze current machine learning methods and techniques and learn about the new machine learning techniques and methods.

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First things first. To grasp the machine learning idea in general, you have to realize what problems and questions can be solved with the aid of ML. For that, we have appointed a few examples of the real-life application of machine learning. Let’s say you have been running an online store for a couple of years now, and you want to estimate your sales level for the coming month. This is a perfect ML assignment. All you have to do is to input all relevant data (previous sales, amount of website visitors, amount of transactions, etc.), and you receive a forecast. The more accurate the data you input, the more precise the prediction you receive.

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And maybe one more instance. You want to develop a new megrim medicine. There are thousands of possibilities and chemical combinations to achieve that. If you’re going to speed this process up considerably, you have to devise a machine learning algorithm that searches for the optimal chemical combination. Generally speaking, machine learning aids the decision-making process, gives relevant insight, and accelerates the pace of work. With this groundwork done, we can switch to the practical machine learning tools and techniques.

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## What are machine learning techniques and methods?

Although the intention behind machine learning is to work without human assistance, to some extent, this assistance is indispensable. To put it in plain language, you have to teach your algorithm how it should work and what it ought to look for. This is exactly what the data scientists do. Does it sound familiar to you? It should! This is how humans learn–from experience. The machine learning algorithms use computational methods to “learn” information directly from available data. This is why it is crucial to input as much relevant data as it’s available. As the number of samples increases, the ML algorithm works more and more efficiently. Machine learning techniques can be divided into two foremost types:

- Supervised
- Unsupervised

The supervised machine learning methods are used when you want to predict or explain the data you possess. The supervised machine learning techniques group and interpret data based only on input data. A supervised algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions[1]. The most popular supervised techniques are classification and regression. For instance, the supervised ML techniques can be used to predict the number of new users who will sign up for the newsletter next month.

On the other hand, the unsupervised machine learning methods find hidden patterns or intrinsic structures in data. They are used to draw inferences from datasets consisting of input data without labeled responses[2]. The unsupervised algorithms group and interpret data solely on input information. The unsupervised ML techniques can be used to aggregate products with similar characteristics, for instance, to simplify the search process in your eCommerce business.

Now, we turn to the specific practical machine learning tools and techniques.

## Practical ML tools and techniques–comparison

It is valid to make a comparison of machine learning techniques because either supervised and unsupervised methods are for different purposes and assignments. And there are also more advanced techniques such as deep learning. We will discuss them a bit further in the text.

### SUPERVISED MACHINE LEARNING TECHNIQUES AND METHODS

The first and, simultaneously, the most important method is called regression. There are five 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. For instance, you can use regression to predict such matters as an employee’s salary, or property value. Regression techniques are divided into simple (simple linear regression) and complex (other four).

The second common type of supervised technique is classification. The classification of machine learning techniques predict or explain a class value. They estimate the probability of an occurrence of an event based on one or more inputs. A couple of examples–with classification, you can divide e-mails into spam and non-spam, assess whether a given image contains a car or a plane, and finally, predict whether a given customer will buy a product (based on their behavior on the website). Usually, the output can be ascribed to two (yes, no) or three (car, plane, none) classes.

What are the differences between regression and classification? The simplest answer is that classification is the task of predicting a discrete class label, whereas regression predicts a quantity.

Last but not least, you also have the ensemble methods. They are based on combining several predictive models in order to receive high-quality predictions. The ensemble methods are a way to reduce the variance and bias of a single machine learning method. You see, a single model may be accurate under certain conditions but inaccurate under other conditions. When you combine two or more models, the quality of the predictions goes up.

### UNSUPERVISED MACHINE LEARNING METHODS

There are two principal unsupervised models–clustering and dimensionality reduction. The clustering method is aimed at grouping or clustering observations that have similar characteristics. The most popular clustering method is the K-Means method, where “K” represents the number of clusters that the data scientist chooses to create. “K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.”[3] In general, the K-means method assigns each data point to the closest of the randomly created centers and re-computes the center of each cluster[4].

The second unsupervised ML technique is called dimensionality reduction. It is the process of reducing the number of random variables taken into consideration by the machine learning algorithm by obtaining a set of principal variables. It can be divided into feature selection and feature extraction. In other words, this method is used to eliminate the least important information from a dataset, for instance, needless or redundant columns, rows, and pixels that are inessential in your analysis. Why is that valid? Generally speaking, the higher is the number of features or other pieces of data, the harder it gets to work on a specific issue. That’s why the data scientists try to remove irrelevant or redundant information from a dataset.

These are the most widespread machine learning methods. However, as the machine learning develops, new techniques come into play. Currently, we can identify at least three new machine learning techniques. They are deep learning along with neural networks, transfer learning, and reinforcement learning. Let’s take a closer look at each one of them.

## The new machine learning techniques and methods

These machine learning methods are much more advanced and sophisticated. Their development is highly promising, as more and more new applications are doable.

### DEEP LEARNING AND NEURAL NETWORKS

The objective of neural networks and deep learning is to capture non-linear patterns in data by adding layers of parameters to the model. In other words, you can think of deep learning as of improvement of traditional machine learning, consisting of more layers that permit higher levels of abstraction and improved predictions from input data. The deep learning algorithms use neural networks to find associations between a set of inputs and outputs. Therefore, the neural networks are composed of input, hidden, and output layers.

For instance, let’s take two pictures, one depicting a cat and one depicting a dog. This is our input. This information is passed between several network layers through the specific mathematical function. When that part is done, you receive the output. For example, information that picture 1 contains a dog in it.

Notwithstanding, deep learning techniques require a lot of data and a lot of computing power. In order to start and run the deep learning algorithms efficiently, you need very powerful computers enhanced with GPUs (Graphical Processing Units).

How can deep learning be used? It is predicted to grow very rapidly, but so far, it has been established that, for instance, it works brilliantly with image analysis and face recognition. We can expect to see more applications in the following years.

### TRANSFER LEARNING

This is a complementary one of machine learning techniques and methods to the previous one. It’s simply the re-trained neural network, adjusted to a new (usually similar) task. By adding a few new layers and adjusting existing ones, the neural network can learn and adapt to the new task. This is a huge time saving and improvement of work since you don’t have to build an entirely new network from scratch. It’s a point where the self-tuning and self-learning capabilities of the deep learning networks come to the fore.

### REINFORCEMENT LEARNING

This is the last of machine learning techniques and methods to analyze. RL is a machine learning method that helps in learning from experience. Reinforcement learning makes a case for itself when you have little or no historical data at all about a problem. The RL algorithms don’t need any information in advance, ergo they learn from data during the process. The reinforcement learning algorithms are eagerly used in games, for instance, chess or GO.

We saw one of the most recognizable applications of RL back in 2017 when a Google computer program called AlphaGo beat the world’s best player in GO, the game many consider the world’s most sophisticated board game. AlphaGo has a self-teaching AI and simply reinforces the progress and power of artificial intelligence to handle the highly complex task of playing GO[5]. As the duel’s result shows–it works properly.

As you can see, there are a number of machine learning techniques, some are relatively fresh, and they develop rapidly. Especially deep learning is a promising one, as there are lots of possible applications. The AI world is continuously in motion. To keep yourself up to date, we encourage you to drop by here as often as possible!

If you are thinking about implementing AI or business intelligence to your business–drop us a line. We are always happy to assist you in coping with your business challenges and ideas. Artificial Intelligence can be the next significant milestone in your company’s history. Let us prove it!

## References

[1], [2] Mathworks. *What Is Machine Learning?*, URL: https://www.mathworks.com/discovery/machine-learning.html. Accessed Jan 10, 2020.

[3] Wikipedia. *k-means clustering*. URL: https://en.wikipedia.org/wiki/K-means_clustering. Accessed Jan 10, 2020.

[4] Jorge Castañón. *10 Machine Learning Methods that Every Data Scientist Should Know*. May 1, 2019. URL: https://towardsdatascience.com/10-machine-learning-methods-that-every-data-scientist-should-know-3cc96e0eeee9. Accessed Jan 10, 2020.

[5] Paul Mozur. *Google’s A.I. Program Rattles Chinese Go Master as It Wins Match*. May 25, 2017. URL: https://www.nytimes.com/2017/05/25/business/google-alphago-defeats-go-ke-jie-again.html. Accessed Jan 10, 2020.