Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform tasks without using explicit instructions by relying on patterns and inference. It is seen as a subset of Artificial Intelligence (AI) (wikipedia.com).
The use of AI requires a lot of resources and an essential technology to driving improvements in quality, efficiency, and speed. However, it has significant impact on performance on all industries and can enable a company achieve a sustainable competitive edge in its market place, it is therefore not surprising that Google, Amazon, Microsoft, Apple, IBM, Facebook and other tech giants are investing in Machine Learning and AI infrastructures as crucial parts of their technologies. These tech giants ultimately want to create better products and services for customers by automating tasks in areas that machines can outperform human beings.
AI is viewed as an essential technology to driving improvements in quality, efficiency, and speed According to Tractica’s analysis (tractica.com). Annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025 In this article, we'll explain the types of machine learning algorithms.
Types of Machine learning Algorithms
Supervised machine learning: Supervised machine learning algorithms seeks to uncover insights, patterns, and relationships from a historical dataset, thus, a labelled training data is used to learn how the input variables are linked to the output or target value you want to classify. During training, the correct answers to the problem being solved is provided to the algorithm as a result, the algorithm is able to learn how the rest of the features relate to the target value, and this enables you to uncover insights and make predictions about possible future outcomes based on the historical data provided. The learning algorithm applies regression and classification models to solve the outcome The algorithm applies the following model: Regression, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbours(KNN).
Unsupervised machine learning: Unsupervised machine learning algorithms explores the data to infer patterns from the dataset without relying on known or labeled outcomes provided. Unlike the supervised machine learning, unsupervised machine learning cannot be applied to situations that require techniques that use regression or a classification because of the use of unlabeled training data to model the underlying structure, explore the data to determine patterns and extract valuable insights.
Unsupervised machine learning is best suited when you do not have data on the outcome, for instance, when determining a target market for an entirely new product to be launched. However, if you are trying to get a better understanding of your existing consumer base, using supervised learning algorithm will be the appropriate choice. The learning algorithm applies association and clustering models to solve the outcome The algorithm applies the following model: K-Means clustering, Principal Component Analysis.
Reinforcement machine learning: Reinforcement learning is a type of machine learning algorithm that allows the agent or system to decide on the next best action based on its current state, by learning behaviors from its environment that has the objective of maximizing rewards from a chosen target. Reinforcement algorithms learn optimal actions and behaviors through trial and error. This is applied in robotic engineering. In robotic engineering, a robot can learn to avoid collisions by receiving negative feedback after collision; in video games, trial and error reveals specific movements that can shoot up a player’s rewards. The agent can then use these rewards to understand the optimal state of game play and choose the next action. Positive reward signal encourages continuing performance a particular sequence of action Negative reward signal penalizes for performing certain activities and urges to correct the algorithm to stop getting penalties.
The use of AI requires a lot of resources and an essential technology to driving improvements in quality, efficiency, and speed. However, it has significant impact on performance on all industries and can enable a company achieve a sustainable competitive edge in its market place, it is therefore not surprising that Google, Amazon, Microsoft, Apple, IBM, Facebook and other tech giants are investing in Machine Learning and AI infrastructures as crucial parts of their technologies. These tech giants ultimately want to create better products and services for customers by automating tasks in areas that machines can outperform human beings.
AI is viewed as an essential technology to driving improvements in quality, efficiency, and speed According to Tractica’s analysis (tractica.com). Annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025 In this article, we'll explain the types of machine learning algorithms.
Types of Machine learning Algorithms
Supervised machine learning: Supervised machine learning algorithms seeks to uncover insights, patterns, and relationships from a historical dataset, thus, a labelled training data is used to learn how the input variables are linked to the output or target value you want to classify. During training, the correct answers to the problem being solved is provided to the algorithm as a result, the algorithm is able to learn how the rest of the features relate to the target value, and this enables you to uncover insights and make predictions about possible future outcomes based on the historical data provided. The learning algorithm applies regression and classification models to solve the outcome The algorithm applies the following model: Regression, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbours(KNN).
Unsupervised machine learning: Unsupervised machine learning algorithms explores the data to infer patterns from the dataset without relying on known or labeled outcomes provided. Unlike the supervised machine learning, unsupervised machine learning cannot be applied to situations that require techniques that use regression or a classification because of the use of unlabeled training data to model the underlying structure, explore the data to determine patterns and extract valuable insights.
Unsupervised machine learning is best suited when you do not have data on the outcome, for instance, when determining a target market for an entirely new product to be launched. However, if you are trying to get a better understanding of your existing consumer base, using supervised learning algorithm will be the appropriate choice. The learning algorithm applies association and clustering models to solve the outcome The algorithm applies the following model: K-Means clustering, Principal Component Analysis.
Reinforcement machine learning: Reinforcement learning is a type of machine learning algorithm that allows the agent or system to decide on the next best action based on its current state, by learning behaviors from its environment that has the objective of maximizing rewards from a chosen target. Reinforcement algorithms learn optimal actions and behaviors through trial and error. This is applied in robotic engineering. In robotic engineering, a robot can learn to avoid collisions by receiving negative feedback after collision; in video games, trial and error reveals specific movements that can shoot up a player’s rewards. The agent can then use these rewards to understand the optimal state of game play and choose the next action. Positive reward signal encourages continuing performance a particular sequence of action Negative reward signal penalizes for performing certain activities and urges to correct the algorithm to stop getting penalties.
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