What is Machine Learning?

Question

Krishnapriya Rajeev · Accepted Answer

Machine Learning is a branch of artificial intelligence that creates algorithms to enable computers to learn from
data and past experiences. This allows machines to automatically improve their performance over time and make predictions without needing explicit programming. The accuracy of these predictions is directly linked to the amount of data used; more data allows for the creation of better models that can make more accurate predictions.
Machine learning is mainly classified into three types:
Supervised Machine learning: It is a type of machine learning in which the machines are trained using already labeled data and using this data the machines are able to predict the output. Here, the input data is already mapped to the correct output and its goal is to find an accurate mapping function. Example: Regression, Classification.Unsupervised Machine learning: It is a type of machine learning in which the machines are expected to find hidden patterns and insights in the provided data. The machine is provided with an unlabelled dataset and is not supervised. Example: Association, Clustering.Reinforcement Learning: It follows a feedback-based mechanism in which an agent learns by interacting with the environment by performing actions and analyzing the results of their action. Here, the goal of the agent is to receive maximum good feedback by performing correct actions. No labeled data is provided to the agent.and

Aryan Kumar · Answer

Machine learning (ML) is the subset of artificial intelligence (AI) that focuses on building systems that learn—or improve performance—based on the data they consume. Artificial intelligence is a broad term that refers to systems or machines that mimic human intelligence.
The study of methods and models that allow computers to learn from data and enhance their performance on a job without being explicitly programmed is known as machine learning (ML), a branch of artificial intelligence (AI). In other words, it's a technique for teaching machines to recognise patterns and make predictions based on data, much like how people learn via trial and error.
There are different types of Machine Learning algorithms based on their learning approach and the kind of data they process. Here are the three main categories:
Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where the output variable is known. The goal is to learn a mapping between the input and output variables to predict the output for new input data. Some examples of supervised learning algorithms include Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM).
Unsupervised Learning: In unsupervised learning, the algorithm is trained on an unlabeled dataset, where the output variable is unknown. The goal is to find patterns and structures in the data, such as clusters or associations, without any prior knowledge of the data. Some examples of unsupervised learning algorithms include K-Means, Hierarchical Clustering, Principal Component Analysis (PCA), and Association Rule Mining.
Reinforcement Learning: In reinforcement learning, the algorithm learns through interaction with an environment. It receives feedback in the form of rewards or penalties based on its actions, and the goal is to maximize the cumulative reward over time. Reinforcement learning has been successfully used in game playing, robotics, and autonomous driving.
Besides these three categories, there are other subfields of Machine Learning, such as Semi-Supervised Learning, Deep Learning, and Transfer Learning

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What is Machine Learning?

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