Ans: The main difference between supervised and unsupervised machine learning is that supervised learning uses labeled training data, while unsupervised learning works with unlabeled data. In supervised learning, the algorithm learns to map inputs to known outputs. Unsupervised learning finds patterns or structures in data without predefined labels.

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Ans: LLMs typically use a combination of supervised and unsupervised techniques. The initial training is often unsupervised, using vast amounts of unlabeled text data. Fine-tuning and alignment may involve supervised learning with labeled data. Overall, LLMs lean more towards unsupervised learning in their core training approach.

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Ans: Supervised learning involves training a model on a labeled dataset where the desired output is known. The algorithm learns to map inputs to correct outputs, minimizing the difference between its predictions and the proper labels. 

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Ans: Unsupervised learning focuses on discovering hidden patterns or structures in unlabeled data. The algorithm explores the data to find inherent groupings, relationships, or representations without predefined output labels.

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Ans: Use supervised learning when: You have an explicit target variable or outcome to predict Labeled training data is available The goal is to make specific predictions or classifications Use unsupervised learning when: You want to explore data structure without predefined labels The goal is to discover patterns, groups, or relationships Labeled data is unavailable […]

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Ans: Clustering is an unsupervised learning technique. It groups similar data points without predefined labels. The algorithm discovers inherent structures in the data based on similarities or distances between points.

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Ans: Deep learning can be both supervised and unsupervised, depending on the specific architecture and task: Supervised deep learning uses labeled data to train neural networks for tasks like image classification or speech recognition. Unsupervised deep learning, such as autoencoders or generative adversarial networks, learns from unlabeled data to discover patterns or generate new samples.

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Ans: Regression is a supervised learning method that predicts continuous numerical values based on input features. It uses labeled training data to learn the relationship between inputs and outputs.

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