Types of Machine Learning

[princyi]

Supervised, Unsupervised, and Reinforcement Learning Explained When embarking on creating a machine learning model, one of the first choices you'll need to make is the type of learning approach to use. This decision hinges on the kind of data you have and the problem you're tackling. Here’s a simple breakdown of the three primary learning types: Supervised, Unsupervised, and Reinforcement Learning.

1. Supervised Learning** Like Learning with a Teacher - You provide the model with data and the correct answers (labels) for each data point, much like showing a picture of a cat and telling the model it's a cat. When to Use - If you have labeled data and you want the model to learn the right answer from this data. Examples** - Classifying images, speech recognition, self-driving car tasks like identifying pedestrians, bank fraud detection.

2. Unsupervised Learning** Learning on its Own - The model explores a lot of data on its own to find patterns and structures, akin to solving a puzzle without seeing the picture on the box. When to Use** - If you have unlabeled data and you want the model to find patterns or structures in the data. Examples - Generating new songs, writing code, handling massive datasets with unlabeled data.

3.Reinforcement Learning** Learning by Trial and Error - The model learns by interacting with an environment, receiving rewards for right actions and penalties for wrong ones, much like training a pet. When to Use - When you want your model to learn from its interactions and feedback, without needing labeled data. Examples** - Playing video games, robotics, real-time decision-making in self-driving cars.

[Prathamji0609]

Great presentation

[DPRASAD-dp]

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[princyi]

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