ML can be defined as a discipline of Artificial Intelligence (AI) that creates systems that can learn from datasets and make decisions or predictions based on that data. ML develops statistical algorithms that can learn from data and generalize unseen data. Moreover, it can perform tasks without explicit human intervention. ML teaches computers to learn from data and make decisions accordingly. ML algorithms can be trained to recognize patterns in data, classify data, or make predictions about future data. In addition, ML applications are fed with new data, and they can learn independently, grow, develop, and adapt.
ML helps solve problems in different areas:
- Computational finance (credit scores, algorithmic trading).
- Computer vision (facial recognition, motion tracking, object detection).
- Computational biology (DNA sequencing, brain tumor detection, drug discovery).
- Automotive, aerospace, and manufacturing (predictive maintenance).
- Natural language processing (voice recognition).
Types of ML
- Supervised Machine Learning: Algorithms are trained on labeled data, which means that the data has been already categorized or classified. The machine is trained with the input and the corresponding output; therefore, the machine will predict the outcome using the test dataset in subsequent phases. Supervised ML can be classified as Classification and Regression. Classification refers to algorithms that deal with classification problems where the output variable is categorical: yes or no, true or false, male or female, etc., like Spam detection and email filtering. Examples of classification algorithms are the Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm, and Support Vector Machine Algorithm. Regression refers to algorithms that work with regression problems where input and output have a linear relationship; they predict continuous output variables like weather prediction, market trend analysis, etc. Examples of regression algorithms are Simple Linear Regression Algorithms, Multivariate Regression Algorithms, Decision Tree Algorithms, and Lasso Regression.
- Unsupervised Machine Learning: Algorithms are trained using unlabeled data, which means that the data is not categorized or classified by grouping the unsorted dataset based on the input’s similarities, differences, and patterns. The machine can predict the outcome without any supervision. Unsupervised ML can be classified as Clustering and Association. Clustering refers to grouping objects into clusters based on parameters such as similarities or differences between objects, like grouping customers by the product they purchase. Examples of clustering algorithms are the K-Means Clustering Algorithm, Mean-Shift Algorithm, DBSCAN Algorithm, Principal Component Analysis, and Independent Component Analysis. Association refers to identifying relationships between the variables of large datasets, like in web usage mining and market data analysis. Examples of Association are the Apriori Algorithm, Eclat Algorithm, and FP-Growth Algorithm.
- Semi-supervised Learning: Semi-supervised learning combines characteristics of both supervised and unsupervised learning.
- Reinforcement Learning: Algorithms learn by interacting with the environment through the hit-and-trial method, acting, learning from experience, and improving performance. In addition, it receives feedback by rewards or punishment, like playing a game or controlling a robot. Examples of reinforcement learning are Positive Reinforcement Learning and Negative Reinforcement Learning. Positive Reinforcement Learning refers to adding a reward after a reward so the behavior may occur again later in the future. Negative Reinforcement Learning refers to strengthening a specific behavior that avoids negative outcomes.
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