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Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on building systems that can learn from and make decisions based on data. It involves training algorithms to recognize patterns and make predictions or decisions without being explicitly programmed to perform specific tasks.
- Supervised Learning: The algorithm is trained on labeled data, meaning it learns from examples that have known outcomes.
- Unsupervised Learning: The algorithm is given unlabeled data and must find patterns and relationships on its own.
- Semi-Supervised Learning: A combination of both supervised and unsupervised learning, where the algorithm is trained on a small amount of labeled data and a large amount of unlabeled data.
- Reinforcement Learning: The algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
- Healthcare: Predicting patient outcomes, personalizing treatment plans, and diagnosing diseases.
- Finance: Fraud detection, algorithmic trading, and credit scoring.
- Retail: Personalized recommendations, demand forecasting, and customer segmentation.
- Autonomous Vehicles: Real-time decision-making, object detection, and path planning.
- Natural Language Processing (NLP): Sentiment analysis, language translation, and chatbots.
- Data Quality: Ensuring the data used for training is accurate and representative.
- Bias and Fairness: Addressing biases in data and algorithms to ensure fair and ethical outcomes.
- Scalability: Developing algorithms that can handle large datasets efficiently.
- Interpretability: Making ML models transparent and understandable to users.
