Machine learning (ML) is a subset and an application of Artificial Intelligence (AI) that allows computers to develop pattern recognition mechanisms used to learn on their own. ML is the ability of continuous learning based on predictions, the ability to make such predictions based on data, and the ability to make appropriate adjustments without any additional programming.

Machine learning, one of the forms of artificial intelligence, effectively automates the process of building analytical models and allows computers to independently adapt to new scenarios.

Nowadays, machine learning is used pretty much everywhere.

Here are just a few examples:

Pinterest uses ML to show you the most interesting content. Yelp relies on machine learning to sort photos that users upload. Next Door applies machine learning algorithms to sort the contents of their bulletin boards. Disqus runs a machine learning system that filters out comments that are spam.

To utilize its power, you should research multiple methods and algorithms of machine learning and then apply it correctly in your business. In this article, I will explain the main types of machine learning algorithms and describe a few ML methods you may apply to your business.

Note: To learn more about Squadex ML services, please check out our Machine Learning Consulting page.

Method 1. Supervised Learning

Supervised learning is usually used for big data sets.

Let’s illustrate this with an example:

There are many photos of different fruit with tags (labels) specifying which is which (e.g. a mango vs. an apple or orange). Data scientists program an algorithm to help the machine recognize different fruits featured in a new data set. The machine automatically identifies the signs by which it can see the difference between mangos, apples, and oranges.

Alternatively, this algorithm can be trained to tell men from women, and so on.

Now take a look at some supervised learning algorithms.

Decision Tree

The decision tree is a tree graph model that simplifies the process of making important decisions. The algorithm provides possible options, the probability of something to happen, and the reliability of the answer. The decision tree usually includes a certain amount of programmed yes/no questions based on which it come up with the answer.

The algorithm analyzes input data and processes it through the programmed questions, answers, and probabilities. In decision making, it provides a final user with the structured answer based on its own logic.

Example of algorithms of machine learning: Decision Tree

 

Naive Bayes Classification

The Naive Bayes Classification follows the Bayes theorem by assuming that features have to be independent. The NBA supposes that the presence of a characteristic in a class is not related to the presence of any other characteristic.

This basically means that any pet can be considered a dog if it has some fur, four legs, and a tail. Even if these signs depend on each other or on other signs, in any case, they make an independent contribution to the likelihood that this pet is a dog. That’s why this algorithm is considered to be unsophisticated.

The Bayes Theorem can be used to:

  • Reveal and weed out spam in emails
  • Sort news articles by their subject
  • Define emotional characteristics of text
  • Recognized characteristics of a human face in face recognition software (Facebook, camera apps)

Linear Regression

Linear regression is generally used to predict continuous numeric variables. A modified version of a linear regression which is implemented in the analytical platform also allows resolving the problem of classification.

Algorithms of Machine Learning: Linear Regression

The linear model is transparent and understandable for the analyst. Based on the available regression coefficients, it is possible to find out how a particular factor influences the result and make conclusions based on that.

It can forecast sales, stock values (i.e. analyzes previous company’s net profit, income, and the profitability of the company’s revenue), and web resources load (relevant if computing resources are stored in the cloud).

Logistic Regression

The main goal of logistic regression is to analyze connections between several independent variables (also known as regressors or predictors) and the dependent variable. Logistic regression can be applied if the input and output variables are continuous.

Binary logistic regression is used when the dependent variable is binary — it can take only two values. Using logistic regression, it is possible to estimate the probability that a given event will occur for a specific test subject.

This algorithm is actively used to:

  • Predict if an individual can or cannot return a loan
  • Figure out whether an individual is healthy or sick
  • Forecast how much revenue can a particular product generate
  • Find out the probability of an earthquake

Logistic regression; use cases of machine learning

Medecision, a pioneer in care management, uses logistic a regression algorithm to calculate risk factors for various diseases. For instance, the algorithm can identify a specific amount of variables that can be used to conclude if a patient with diabetes needs hospitalization or not.

Support Vector Machine

The support vector machine (SVM) method is a set of algorithms used for classification and regression analysis tasks. Considering that in an N-dimensional space each object belongs to one of two classes, the SVM generates an N-1-dimensional hyperplane to classify these points into two groups. Imagine if you could depict points of two different types on paper that can be linearly divided.

Besides the fact that the method performs the separation of objects, SVM selects the hyperplane in that way, that it is characterized by the maximum distance from the nearest element of each of the groups.

Among the most crucial problems that were solved using the method of supporting objects (and its modified implementations) are the display of advertising banners on sites, gender recognition based on photos and splicing of human DNA.

Ensemble Method

The ensemble method is based on training algorithms that form multiple classifiers and then segment new data points by starting from voting or averaging.

The original ensemble method is like Bayesian averaging, but later algorithms include error correction of the output coding, bagging, and boosting.

Boosting is aimed at turning weak models into strong ones by building an ensemble of classifiers. Bagging also aggregates improved classifiers, but it uses parallel learning of basic classifiers.

Here are a few applications of the ensemble method:

  • It minimizes the effect of randomness. The aggregated classifier “averages” the error for each of the basic classifiers.
  • The ensemble method reduces dispersion. The aggregate opinion of the entire multitude of models is better than the opinion of an individual model.
  • It prevents going beyond the set. When building a combined hypothesis by any means (logistic regression, averaged value, voting, etc.), the set of hypotheses expands; therefore, the result obtained does not go beyond it.

Method 2. Unsupervised learning

Though there is a lot of labeled data, even larger amount of data still exists without labels (not tagged). These are images without titles, audio recordings without comments, texts without annotations.

Unsupervised learning finds the links between separate data, identifies similar patterns, chooses patterns, organizes data or describes its structure, and performs data classification.

Unsupervised learning is used, for example, in recommendation systems. The algorithms of machine learning look at a user’s purchase history to provide a few product or service suggestions — something similar or additional accessories.

Similarly, YouTube’s ML algorithms analyze the videos a user has watched to display video suggestions that are somewhat similar to previously viewed.

Now let’s take a look at unsupervised learning algorithms.

Clustering Algorithms

Clustering algorithms classify a given set of objects into groups (clusters) by placing similar objects in one cluster and objects that have different characteristics in other clusters.

Clustering simplifies further data processing by splitting the set of objects into groups of similar objects to work with each class separately, like tasks of classification, regression, and forecasting.

The algorithm reduces the amount of stored data by leaving one representative of the object from each cluster (data compression tasks).

Clustering identifies atypical objects that do not fit in any of the clusters (single-class classification tasks) and build a hierarchy of objects (taxonomy tasks). Clustering is broadly applied in image segmentation, marketing, anti-fraud, forecasting, and text analysis. For example, Sift detects online fraud and prevents paid reviews through the application of algorithms of machine learning.

Principal Component Method

The principal component method simplifies the complexity of high-dimensional data while maintaining trends and patterns. It does this by compressing data, which acts as a summary of functions. Data as such is common in different branches of science and technology and pops up when several characteristics are measured for each sample, for example, such as the expression of many species.

This type of data presents problems caused by an increased error rate due to multiple data correction. The method is similar to clustering — it finds templates without references and analyzes them by checking if samples are taken from different research groups and if they feature significant differences.

Principal component method can be applied incorrectly. Scaling variables can end up generating different analysis results. It is key that it is not adjusted for compliance with the previous data value.

Method 3. Reinforcement Learning

This area of machine learning studies the behavior of intelligent agents acting in a particular environment and making decisions.

The response of the environment to the decisions taken are reinforcement signals based on which the agent is trained. Therefore, such training can be described as “teaching with a teacher,” where the teacher is the environment or its model (experimental system).

Method 4. Artificial Neural Network

Artificial Neural Network represents a system (or a structure) consisting of artificial neurons. They are connected and interact with each other. These neurons are based on simple processors.

The processor gets the signal from a group of processors and then sends them to another one. They are all connected to a single network.

Algorithms of Machine Learning: Neural Network

The neurons are placed on different levels, where the first layer is on the input level and the second layer is on output level. Input sensors receive data from outer space, process it and then transmit the impulses to neurons and to the output level.

There is a hidden level between these to (the one that processes data), but it is not directly connected to input and output levels. They can find simple relativity in data sets.

Method 5. Deep Learning

Deep learning recreates human abstract thinking, empowering the machine to generalize the parameters.

Here is how it works (illustrated by an example below):

A supervised neural network poorly understands handwriting that differs from one person to another. To improve the results, the machine has to be fed with different variations of handwriting. Only then the machine will be able to correctly recognize and understand what is written on paper.

Deep learning is actively used during interaction with artificially-created multi-layer networks.

Let’s say that we show the robot a few photos of women and men. Initially, the neural network is trained only to recognize differences in brightness. Then, it will be able to discern circles and angles. Moving forward, it will be able to figure out that a human being is on the photo (without identifying their sex).

With each subsequent circle, recognizable images become more complex on purpose. Using the neural network, the machine independently generates specific forms and shapes to determine important visual forms and even classify them (if required).

Eventually, the program will begin to understand the images better. It will be able to figure out if this or that photo features a man or a woman and sort the photos by sex.

Conclusion

Multiple algorithms of machine learning are widely used in different industries and in various applications, both by enterprises like Amazon or Facebook, and by small and medium businesses. Facial recognition, search engines, machine translation, recommendation systems — just to name a few.

Utilizing the power of specific machine learning algorithms, you can generate forecasts and predictions (and much more).

For example, by analyzing financial data machine learning can help us predict stock values. A system that collects data about patients can help physicians predict the risks for their patients’ health.

Algorithms of machine learning can and should be applied wherever the data volumes are so large that it is impossible to set any general and simple rule for its processing. By choosing the correct algorithm, you can manage data overall, and Big Data specifically, much more efficient.

Looking to empower your business through Machine Learning but do not know where and how to start?

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