Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that involves training algorithms to learn from data and make predictions or decisions based on that data. Here are some common tools of machine learning:
8 Common Tools of Machine Learning
Supervised Learning:
This is a type of machine learning that involves training a model on a labeled dataset to predict outcomes for new, unseen data.
Unsupervised Learning:
This is a type of machine learning that involves training a model on an unlabeled dataset to discover patterns and relationships in the data.
Reinforcement Learning:
This is a type of machine learning that involves training a model to make decisions based on feedback from its environment. It is often used in robotics and game development.
Deep Learning:
This is a subset of machine learning that uses artificial neural networks to learn from large datasets. It is commonly used for tasks like image recognition, speech recognition, and natural language processing.
Decision Trees:
This is a type of supervised learning algorithm that uses a tree-like model of decisions and their possible consequences.
Support Vector Machines (SVMs):
This is a type of supervised learning algorithm that separates data points into different categories using a hyperplane.
K-Nearest Neighbor (KNN):
This is a type of supervised learning algorithm that classifies new data points based on the classification of nearby data points.
Random Forests:
This is an ensemble learning method that uses multiple decision trees to improve the accuracy of predictions.
These are some of the most common tools of machine learning, but the field is constantly evolving, and new tools and techniques are being developed all the time.
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