Unsupervised Learning
• Unsupervised Learning: This is like learning without a teacher. The AI is given data without any correct answers and has to find hidden patterns or groups on its own.
• K-Means Clustering: An algorithm that sorts data into groups based on their similarities. It's like a teacher telling students to form groups with others who have similar hobbies without telling them what those groups are.
• Dimensionality Reduction: This is the process of simplifying data by reducing the number of features or variables while keeping the most essential information. It's like summarizing a long paragraph to get the main idea.
• Optical Character Recognition (OCR): This is technology that "reads" text from images or scanned documents. It converts the text in the image into a digital format that can be edited and searched.
Reinforcement Learning
• Reinforcement Learning: The AI learns by trying things out and getting a reward for good actions or a penalty for bad ones, like a dog learning tricks with treats. It's a "trial and error" method.
• Naive Bayes: A simple but powerful algorithm that works surprisingly well for tasks like classifying text or emails. It's based on probability and assumes that features are independent of each other.
• Overfitting: This happens when an AI learns the training data so well that it fails on new, unseen data. It's like a student who memorizes test answers but doesn't understand the concepts and fails on a slightly different test.
• Generative Adversarial Networks (GANs): A system of two AIs working against each other. One creates fake images or videos, and the other tries to spot the fakes. This process helps the generator create very realistic-looking content.
Transfer Learning
• Transfer Learning: Reusing knowledge from a previously trained AI model to help with a new, related task. It’s like a person who already knows how to play the guitar learning the ukulele much faster because of their existing skills.
• Principal Component Analysis (PCA): A way to shrink large datasets while keeping the main patterns. It simplifies complex information, making it easier for AI models to work with.
• Data Augmentation: Boosting an AI's learning by creating slightly altered copies of existing data, like rotating or flipping an image. This helps the AI learn more effectively without needing to collect more data.
• Vision Transformers (ViTs): A new kind of AI model for images. Unlike older models, ViTs look at images in a way similar to how they process text, which has made them very powerful for computer vision tasks.

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