In machine learning, the term “learning” refers to the process through which machines examine current data and gain new skills and information from it. Machine learning systems employ algorithms to search for patterns in datasets that may include structured data sets, unorganized textual data, numeric data, or even rich media such as audio files, photos, and videos. Machine learning algorithms are computationally expensive, necessitating the need for specialized infrastructure to run at scale.
What is Machine Learning?
Machine learning is a branch of study that aims to train machines to do cognitive tasks in the same way that humans do. While they have far fewer cognitive abilities than the ordinary person, they are capable of quickly processing large amounts of data and extracting significant commercial insights.
Machine learning algorithms employ computational methods to “learn” information directly from data rather than depending on a model based on a preconceived equation. As the number of samples available for learning grows, the algorithms alter their performance. Deep learning is a type of machine learning that is overspecialized.
As a result, as a machine learning practitioner, you may come across a variety of forms of learning, ranging from entire fields of research to individual methodologies.
( Suggested Read- Basics of Machine Learning )
Different Methods of Machine Learning
The main branches of machine learning are listed below. The majority of machine learning algorithms and techniques fall into one of the following groups of methods:
( Related – Types of Machine Learning Methods )
- Supervised Learning
The term “Supervised Learning” refers to a scenario in which a model is used to learn a mapping between input samples and the target variable. If you know what you want to teach a machine beforehand, use Supervised Learning.
This usually entails exposing the algorithm to a large amount of training data, allowing the model to study the output, and fine-tuning the parameters until the desired results are obtained. The machine can then be put to the test by allowing it to generate predictions for a “validation data set,” or new data that hasn’t been seen before.
- Un-Supervised Learning
Unsupervised learning allows a machine to study a set of data without the assistance of a human. Following the initial exploration, the computer attempts to uncover hidden patterns that link various variables. This method of learning can assist in the classification of data into categories based solely on statistical attributes.
Unsupervised learning does not require big data sets for training, making it significantly faster and easier to implement than supervised learning. Unsupervised learning, in contrast to supervised learning, is based solely on the input data, with no outputs or target variables. As a result, unlike supervised learning, unsupervised learning does not have a teacher correcting the model.
- Semi-Supervised Learning
Semi-supervised learning is supervised learning with a small number of labeled instances and a large number of unlabeled examples in the training data.
In contrast to supervised learning, the purpose of a semi-supervised learning model is to make good use of all available data rather than just the labeled data.
Unsupervised and supervised learning techniques are used in semi-supervised learning. Manually categorizing part of the data, for example, can provide an example to the algorithm for how the rest of the data set should be sorted.
- Reinforcement learning
Reinforcement learning is a technique that allows a machine to interact with its surroundings. Playing a video game repeatedly and rewarding the algorithm when it does the required action is a simple example. The machine can eventually learn from its experience by repeating the operation thousands or millions of times. The model has some response from which to learn, similar to supervised learning, albeit the feedback may be delayed and statistically noisy, making it difficult for the agent or model to connect cause and effect.
Playing a game in which the player has the objective of earning a high score and can take actions in the game while receiving feedback in the form of punishments or rewards is an example of a reinforcement problem.
Related blog – What is Supervised, Unsupervised, and Reinforcement Learning
Top Machine Learning Techniques
Now Let’s have a look at some of the most popular machine learning techniques that fall under the above-mentioned categories of Machine learning Methods.
- Regression
When the output is a real or continuous value, regression techniques are typically employed to generate predictions on numbers. It uses training data to predict new test data since it falls under the category of Supervised Learning. The purpose of regression techniques is to use a previous data set to explain or forecast a certain numerical result. In the case of retail demand forecasting, regression algorithms can use previous pricing data and anticipate the price of a similar property.
Continuous reactions, such as changes in temperature or fluctuations in power consumption, are predicted using regression algorithms. Electricity load forecasting and algorithmic trading are two examples of typical applications.
If you’re working with a data range or the nature of your response is a real number, such as temperature or the time until a piece of equipment fails, use regression techniques.
The simplest and most fundamental method is linear regression. The following equation is used to model a dataset in this case: ( y = m * x + b )
Multiple pairings of data, such as x, y, can be used to train a regression model. To do so, you must first establish a position for the line, as well as its slope, with a minimum distance from all known data points. This is the line that best approximates the data’s observations and can be used to produce predictions for fresh data that hasn’t been seen before.
According to Educuba, Following are some of the most commonly used algorithms in the Regression Technique.
- Simple Linear Regression Model
- Lasso Regression
- Logistic Regression
- Support Vector Regression
- Multivariate Regression Algorithm
- Multiple Regression Algorithm
( Also Read – Working of Linear and Logistic Regression Model )
- Classification
A classification model is a Supervised Learning method that generates a conclusion from observed values as one or more categorical outputs. Many AI applications require classification, but it is especially beneficial for eCommerce applications. Classification algorithms, for example, can aid in predicting whether or not a buyer will purchase a product. In this situation, the two classifications are “yes” and “no.” Classification algorithms are not limited to two classes and can be used to categorize materials into many different groups. The Classification model employs a variety of methods, including Logistic Regression, Multilayer Perception, and others. In this model, we classify our data into distinct categories and assign labels to those categories. There are two types of classifiers:
Classifiers with two unique classifications and two outputs are known as Binary classifiers.
Classifiers having more than two classes are known as Multi-class classifiers.
- Clustering
Clustering is a Machine Learning approach for categorizing data points into distinct groups. If we have a set of objects or data points, we can use a clustering method to analyze and group them based on their traits and characteristics. Because of its statistical approaches, this unsupervised procedure is applied. Cluster algorithms use training data to make predictions and form groups based on resemblance or unfamiliarity.
( Related – Applications of Clustering )
Unsupervised learning approaches include clustering algorithms. K-means clustering, mean-shift, and expectation-maximization are three popular clustering techniques. They categorize data points into groups based on features that are similar or shared.
When huge amounts of data need to be segmented or categorized, grouping or clustering techniques are very effective in business applications.
Some of the Clustering Methods are given below-
- Density-based methods
- Hierarchical methods.
- Partitioning methods
- Grid-based methods
( Read further on this – Types of Clustering Algorithm )
- Decision Tree
It’s a supervised learning algorithm that’s commonly used to solve classification difficulties. It works for both categorical and continuous dependent variables, which is surprising. We divide the population into two or more homogenous sets using this approach. This is done to create as many separate groups as feasible based on the most important attributes/independent variables.
According to MobiDev, The Decision tree algorithm categorizes objects by responding to “questions” regarding their qualities at nodal points. One of the branches is chosen based on the answer, and another question is presented at the next junction until the algorithm reaches the tree’s “leaf,” which represents the ultimate answer.
Knowledge management platforms for customer service, predictive pricing, and product planning are examples of decision tree applications.
( Also Read – Decision Tree in Machine Learning )
- Neural Networks
Neural networks are designed to resemble the brain’s structure: each artificial neuron connects to many other neurons, and millions of neurons work together to form a sophisticated cognitive structure. The structure of neural networks is multilayer: neurons in one layer transfer data to many neurons in the next layer, and so on.
The data eventually reaches the output layer, when the network decides how to deal with a problem, categorize an object, and so on. The study of neural networks is characterized as “deep learning” because of its multi-layer structure.
Neural networks can be used for machine translation, fraud detection, and virtual assistant services in the telecoms and media industries. They’re used in the financial industry to detect fraud, manage portfolios, and assess risk.
( Related – Applications of Neural Networks )
- Anomaly Detection
The process of recognizing unexpected items or events in a data set is known as anomaly detection. Detection of fraud, failure detection, computer health monitoring, and more applications employ this technology. Anomaly detection can be divided into three categories:
- Point anomalies: When a single piece of data is unexpected, this is referred to as a point anomaly.
- Contextual anomalies: Contextual anomalies are anomalies that are context-specific.
- Collective anomalies: When a group or collection of linked data elements is anomalous, it is referred to as a collective anomaly.