Supervised Learning
Learning from labeled data
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- Supervised Learning involves training models on labeled datasets to predict outcomes for new, unseen data.
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Unsupervised Learning
Finding patterns in unlabeled data
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- Unsupervised Learning identifies hidden structures in data without predefined labels, such as clustering and dimensionality reduction.
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Semi-Supervised Learning
Combining labeled and unlabeled data
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- Semi-Supervised Learning uses a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy.
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GPT
Generative Pre-trained Transformer
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- GPT models generate human-like text by predicting the next word in a sequence using transformer architecture.
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Random Forest
Ensemble of decision trees
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- Random Forest builds multiple decision trees and combines their outputs for robust predictions.
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AI Ethics
Guidelines for responsible AI
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- AI Ethics addresses fairness, transparency, and accountability in AI systems to prevent harm and bias.
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Gradient Boosting
Sequential ensemble method
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- Gradient Boosting builds models sequentially, correcting errors of previous models for improved accuracy.
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Explainable AI
Making AI decisions understandable
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- Explainable AI provides insights into how models make decisions, improving trust and compliance.
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XGBoost
Optimized gradient boosting
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- XGBoost is a high-performance implementation of gradient boosting for structured data.
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