Supervised vs Unsupervised vs Semi-Supervised vs Reinforcement Learning – What's the Difference?
Understanding the types of machine learning is foundational for anyone in AI, data science, or analytics. But with so many terms floating around, it's easy to feel overwhelmed.
✅ This video offers a clear and concise comparison of:
🔹 Supervised Learning – Labeled data & prediction
🔹 Unsupervised Learning – Finding hidden patterns
🔹 Semi-Supervised Learning – Best of both worlds
🔹 Reinforcement Learning – Learning by interaction & reward
📺 Watch the video here:
🔗 https://youtu.be/S5xakmeQGJI
Perfect for students, professionals, or anyone brushing up on their ML fundamentals!
#MachineLearning #DataScience #AI #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #MLAlgorithms #ArtificialIntelligence #TechEducation #DeepLearning
From a website, https://www.stratascratch.com/blog/supervised-vs-unsupervised-learning/, 'In a supervised learning algorithm, we need to provide the ground truth label for each data point during the training process of the model.' And 'In contrast with supervised learning, we don’t need to provide the model with the ground truth label of each data point during the training process. This means that the model will learn the pattern of data points by itself, hence the name ‘unsupervised’.'. If you visit the website, I think it may cover all of the parts of your question. Good luck with your research.
Supervised, unsupervised, semi-supervised, and reinforcement learning are four main types of learning in AI. Supervised learning uses data that has labels or correct answers, helping the model learn from examples. For example, if we give an AI pictures of fruits with names, it learns to identify them. Unsupervised learning has no labels, so the AI finds patterns or groups by itself, like grouping customers based on shopping habits. Semi-supervised learning is a mix of both — it uses a small amount of labeled data and a large amount of unlabeled data, which helps when labeling is hard or expensive. Reinforcement learning teaches the AI through trial and error, where it learns by receiving rewards or penalties for its actions, like a robot learning to walk or an AI playing a game to improve its score. Each type of learning is used based on the goal and available data.
Supervised - you tell the system the correct answer.
Reinforcement - you tell the system how well they did (grade), not the answer.
Unsupervised - you tell the system nothing (just let it identify patterns in the data).
Semi-supervised - supervised for some of the data, unsupervised for other parts of the data.
(BONUS) Active - the system requests answers for specific data points.
Supervised Learning: Data with labels -> Learn a function to label new, unlabeled data. Semisupervised Learning: Data with (least) and without labels (most) -> Learn a function to label new, unlabeled data. Unsupervised Learning: Data without labels -> Identify patterns, structures that help you categorize data. Reinforcement Learning: Agents interact with the environment using a reward function to decide which actions to execute.
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