Machine Learning is becoming more and more predominant in the technology sector. It is vital for everyone who is related to the industry to know how machines can learn on their own. For example, we, as a leading software company in the USA, do it by using algorithms to manipulate data in certain ways−making predictions about the future, providing insights, and learning from them. That is why it is important to know what types of Machine Learning Algorithms are out there.
Types of Machine Learning Algorithms
The ML algorithms are broadly classified into four types−supervised, semi-supervised, unsupervised, and reinforcement Machine Learning Algorithms.
Supervised Machine Learning Algorithms
It is the basic type of Machine Learning Algorithms where the programmer has greater control over the process. The engineer can decide which data he or she feeds into the system and which type of output is expected from the system. The machine must process the given data and provide solutions in the desired manner. For instance, you want to know how much time it will take you to get from your house to a grocery store. So, you input the data like time of day or any other piece of information and the machine will provide you with an outcome − the time you will spend on your way to the store.
The supervised Machine Learning Algorithms involve two processes−classification and regression. In the classification stage, the system decides on the type of information it receives. It must be able to classify these data into different classes or categories, based on some predefined criteria, like “spam” or “not spam”.
During the regression, process the system identifies a pattern in the information and predict continuous outputs. Thus, for instance, it allows Data Scientist to understand sales or marketing seasonal patterns.
Examples
- Linear Regression. It is one of the common machine learning algorithms, and its purpose is to establish a relationship between the dependent and independent variables. The aim is to find out the relationship in the form of the equation−y=ax+b
Where x is the independent variable, y is the dependent variable, and a and b are constants, which represent the slope and intercept of the line respectively.
- Logit Regression. Logit, or logistic regression, aims to predict a discrete value based on the given input value or the independent variable. The discrete value can be in the form of binary odds of 0 or 1, like true or false, etc. In short, the system predicts the probability of true/false or yes/no responses on the basis of the independent variable’s value.
- Random Forest. This ML algorithm is commonly used for both data classification and its regression. The fundamental concept behind the technique is that the more trees you have in the forest, the stronger it will be. In other words, as you keep on feeding data to the algorithm, it will give better and better predictions.
- Neural Networks. This method can be used in almost all machine learning techniques. These are general approximations of functions, which can be used to map the output from the input. The system is inspired by the incomparable biological or human neural networks.
Usage
Machine Learning Algorithms as a machine learning service are widely used in price prediction in fields like sales, commerce, and the stock market. These are the industries which depend a lot on future forecasts, and by using supervised Machine Learning Algorithms, better predictions can be made. Sales platforms such as Highspot and Seismic make use of supervised algorithms.
Unsupervised Machine Learning Algorithms
While in supervised ML learning, the machine is supposed to deliver one of the known results, in the unsupervised type, the result itself is not defined, and the machine has to define and deliver it. It can identify the data structure and extract useful insights from the information and detect patterns in it. The findings from the data will be used for improved efficiency in future tasks.
The first step is clustering, where information is collected into different segments. In the next step, the machine tries to extract the useful information from the aggregate data by reducing its dimensionality.
Examples
- K-means Clustering. In the unsupervised learning algorithms list, it is probably the simplest method. As the name indicates, the idea is to define clusters based on K centers. The K centers are placed in such a way to maximize the difference or distance between each, and any data is assigned to a group with the closest K-centre.
- Principal Component Analysis (PCA). PCA is a procedure where the data is classified into a set of unrelated components called the principal components. It is done with the help of an orthogonal transformation, which is a linear transformation of a vector space that preserves lengths of vectors
- t-Distributed Stochastic Neighbour Embedding. It is a technique commonly used for visualization problems. It is basically used for the reduction of dimensions in high-dimensional problems, like reducing multi-dimensional visualizations into simpler 2D or 3D data.
- Association Rule. This is another commonly used unsupervised technique to find out the relationship between components in an extensive database. It is a rule-based method. For example, you can find interesting relations between the products purchased and the usage of PoS machines in supermarket sales data.
Usage
Unsupervised algorithms are mostly used in digital marketing and advertising. They are used to analyze the customer-centric information available and to improve services according to customer choices and behavior. Besides, it may be utilized to identify the target audience. Salesforce is an excellent example of such usage.
Semi-supervised Machine Learning Algorithms
Semi-supervised learning falls somewhere between the supervised and unsupervised machine learning techniques by incorporating elements of both methods. This method is used when there is only a limited set of data available to train the system, and as a result, the system is only partially trained. The information the machine generates during this partial training is called pseudo data and later on computer combines both labeled and the pseudo-data to make predictions.
Examples
- uClassify. This is one of the most popular machine learning algorithms from the semi-supervised type. uClassify is a classification tool for texts which is free of cost. It can be used to automate email messages, detect spam, classify blog posts, and detect languages.
- GATE. This is a Java tool applied for text processing, and it is currently widely used by scientists, teachers, and students across the world. It can be applied to extract data from multiple languages or for language processing.
Usage
Semi-supervised machine learning finds widespread use in the healthcare industry. It is applied to classify and manage web content, speech identification, and analysis. Another area where it is widely applicable is the regulatory field. Speech and Image analysis is best done with the assistance of this technique.
Reinforcement Machine Learning Algorithms
Reinforcement is a machine learning process closely related to Artificial Intelligence. With the aid of some available labeled and incoming data, the machine learns to reinforce and improve itself over time. It’s a process which is self-sustainable and with each task completed, the system upgrades on its own. It makes use of a feedback loop called exploitation or exploration. It means that the system processes the data, analyzes the results and then improves the process for the next job. The feedback from the outcome can be both negative and positive. The system modifies itself towards the positive result and moves away from the processes, which resulted in negative feedback.
Examples of Reinforcement Machine Learning Algorithms
- Q-Learning. It is a model-free reinforcement machine learning algorithm. It is typically used as a policy advisor. The program can advise you on the best course of action under the given circumstances. Q-Learning aims to recognize the course of action, which maximizes or minimizes a particular value. The results obtained are used to reinforce the process.
- Monte Carlo Tree Search. It is a decision-making algorithm commonly used in board games such as Chess, Shogi and Go. It can also be applied in card and video games like Poker, Bridge and Total War. MCTS makes use of a random method to solve problems, which cannot be addressed in a traditional way.
- Temporal Difference, or TD. It is another model-free random learning method, where the process is done with the help of an estimated value of the function. This value is used to make a further estimate, and the results reinforce the process to reach more accurate results. The process bootstraps on the outcome to improve itself.
- Asynchronous Actor-Critic Agents (AAAC). This is one of the newest among all Machine Learning Algorithms we discussed. It is a deep reinforcement learning program created by Google’s AI division, DeepMind. It is quite simple, understandable, and fast. AAAC can be used in computer gaming to solve some environmental problems.
Usage
Reinforcement learning methods are best used when there is only limited data available or when the accessible information is inconsistent. Its main area of application is the gaming industry. With the aid of machine learning algorithms, the system can adapt to the unpredictable player behavior and adjust the gameplay accordingly. The popular video game series − Grand Theft Auto makes use of this technique in its game design.
The technique is also finding its appliance in self-driving cars. It can detect roads, turns and decide which way to turn. The technology made headlines when the AI software AlphaGo beat the human champion in the board game “Go”. Natural language processing is another common area of application.
It is clear that machine learning is making forays into almost all fields of human endeavor and helping us solve many problems. Whether it is social media, a food delivery app or an online taxi service, we are now very much dependent on it for our everyday tasks.
In case you have a project in mind that requires skills and a solid knowledge of Machine Learning, you can contact LITSLINK to know more about our software development services. We would be thrilled to share our expertise with you and turn your idea into reality.
