Machine learning (ML) is an artificial intelligence (AI) discipline that enables machines to automatically learn from data and previous experiences while identifying patterns to make predictions with minimal human intervention.
Machine learning methods allow computers to function autonomously without the need for explicit programming. ML applications are fed new data and can learn, grow, develop, and adapt on their own.
Machine learning extracts useful information from large amounts of data by using algorithms to recognise patterns and learn in an iterative process. Instead of relying on any predetermined equation that may serve as a model, ML algorithms use computation methods to learn directly from data.
During the ‘learning’ processes, the performance of ML algorithms improves adaptively as the number of available samples increases. Deep learning, for example, is a sub-domain of machine learning that trains computers to mimic natural human traits such as learning from examples. It outperforms traditional ML algorithms in terms of performance parameters.
While machine learning is not a new concept, it has been used since World War II with the Enigma Machine, the ability to apply complex mathematical calculations automatically to growing volumes and varieties of available data is a relatively recent development.
With the rise of big data, IoT, and ubiquitous computing, machine learning has become critical for solving problems in a variety of fields, including:
- Finance through computation (credit scoring, algorithmic trading)
- Vision in computers (facial recognition, motion tracking, object detection)
- Biological computation (DNA sequencing, brain tumour detection, drug discovery)
- Manufacturing, automotive, and aerospace (predictive maintenance)
- Natural language understanding (voice recognition)
How Does Machine Learning Function?
A model is created by moulding machine learning algorithms on a training dataset. When new input data is introduced to the trained ML algorithm, it makes a prediction based on the developed model.
The prediction is then checked for accuracy. The ML algorithm is deployed or trained repeatedly with an augmented training dataset until the desired accuracy is achieved based on its accuracy.
Types Machine Learning
Machine learning algorithms can be trained in a variety of ways, each with advantages and disadvantages. Machine learning is broadly classified into four types based on these methods and modes of learning:
1. Machine Learning With Supervision
This type of ML is supervised, in which machines are trained on labelled datasets and then allowed to predict outputs based on the training. Some input and output parameters are already mapped, according to the labelled dataset. As a result, the machine is trained using the input and output. In subsequent phases, a device is created to predict the outcome using the test dataset.
Consider the following input dataset of parrot and crow images: Initially, the machine is trained to recognise the images, which include the colour, eyes, shape, and size of the parrot and crow. Following training, an image of a parrot is provided as input, and the machine is expected to identify the object and predict the output. To make a final prediction, the trained machine examines the input image for various features of the object, such as colour, eyes, shape, and so on. In supervised machine learning, this is the process of identifying objects.
The supervised learning technique’s primary goal is to map the input variable (a) to the output variable (b). There are two broad categories of supervised machine learning:
Classification algorithms are those that address classification problems with categorical output variables, such as yes or no, true or false, male or female, and so on. This category’s real-world applications include spam detection and email filtering.
- The Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm, and Support Vector Machine Algorithm are some well-known classification algorithms.
- Regression algorithms deal with regression problems in which the input and output variables have a linear relationship. These are well-known predictors of continuous output variables. Weather forecasting, market trend analysis, and other applications are examples.
- The Simple Linear Regression Algorithm, Multivariate Regression Algorithm, Decision Tree Algorithm, and Lasso Regression are all popular regression algorithms.
2. Machine Learning Without Supervision
Unsupervised learning is a learning technique that does not require supervision. In this case, the machine is trained on an unlabeled dataset and is able to predict the output without human intervention. An unsupervised learning algorithm attempts to group the unsorted dataset based on similarities, differences, and patterns in the input.
Consider the following input dataset: images of a fruit-filled container. The images in this case are unknown to the machine learning model. When we feed the dataset into the ML model, the model’s task is to identify and categorise the patterns of objects seen in the input images, such as colour, shape, or differences. Following categorization, the machine predicts the output while being tested with a test dataset.
Unsupervised Machine Learning Is Divided Into Two Types
Clustering refers to the technique of grouping objects into clusters based on parameters such as similarities or differences between objects. For example, grouping customers based on the products they buy.
The K-Means Clustering Algorithm, Mean-Shift Algorithm, DBSCAN Algorithm, Principal Component Analysis, and Independent Component Analysis are some well-known clustering algorithms.
Association learning is the process of identifying typical relationships between variables in a large dataset. It determines the interdependence of various data items and maps the variables associated with them. Web usage mining and market data analysis are two examples of typical applications.
The Apriori Algorithm, Eclat Algorithm, and FP-Growth Algorithm are examples of popular algorithms that follow association rules.
3. Learning That Is Semi-Supervised
Semi-supervised machine learning combines aspects of both supervised and unsupervised machine learning. It trains its algorithms using a combination of labelled and unlabeled datasets. Semi-supervised learning overcomes the disadvantages of the preceding options by using both types of datasets.
Consider the following example of a college student. In college, supervised learning refers to a student learning a concept under the supervision of a teacher. Unsupervised learning occurs when a student learns the same concept at home without the assistance of a teacher. Meanwhile, in college, a student revising the concept after learning under the supervision of a teacher is a semi-supervised form of learning.
Learning Through Reinforcement
Reinforcement learning is a process that is based on feedback. Here, the AI component uses the hit-and-trial method to automatically assess its surroundings, take action, learn from experiences, and improve performance. The component is rewarded for each correct action and penalised for each incorrect move. As a result, the reinforcement learning component seeks to maximise rewards by performing positive actions.
Unlike supervised learning, reinforcement learning does not use labelled data, and agents learn solely through their experiences. Take, for example, video games. The game defines the environment in this case, and each move of the reinforcement agent defines its state. The agent is entitled to feedback in the form of punishment and rewards, which affects the overall game score. The agent’s ultimate goal is to achieve a high score.
Reinforcement learning is used in a variety of fields, including game theory, information theory, and multi-agent systems. Reinforcement learning methods or algorithms are further classified as follows:
- Positive reinforcement learning: Adding a reinforcing stimulus after a specific behaviour of the agent increases the likelihood that the behaviour will occur again in the future, for example, adding a reward after a behaviour.
- Negative reinforcement learning is the process of strengthening a specific behaviour in order to avoid a negative outcome.
