Отправлено: 14.10.24 07:39. Заголовок: Overview of Machine Learning

Machine learning is a branch of artificial intelligence that focuses on building systems that can learn from data and improve their performance over time without being explicitly programmed. It encompasses various techniques and algorithms that allow computers to identify patterns, make decisions, and predict outcomes based on input data.

Here are some key concepts in machine learning:

Supervised Learning: The model is trained on labeled data, meaning the input comes with the correct output. Common algorithms include linear regression, decision trees, and support vector machines.

Unsupervised Learning: The model works with unlabeled data to find hidden patterns or intrinsic structures. Clustering (like k-means) and dimensionality reduction (like PCA) are common techniques.

Reinforcement Learning: An agent learns to make decisions by taking actions in an environment to maximize a reward. This approach is used in areas like robotics and game playing.

Neural Networks: Inspired by the human brain, these are a set of algorithms designed to recognize patterns. Deep learning, a subset of machine learning, uses multi-layered neural networks to tackle complex tasks such as image and speech recognition.

Overfitting and Underfitting: Overfitting occurs when a model learns the training data too well, including its noise, while underfitting happens when a model is too simple to capture the underlying trends.

Evaluation Metrics: Metrics like accuracy, precision, recall, F1 score, and ROC-AUC are used to assess a model's performance.

Feature Engineering: The process of selecting, modifying, or creating new features to improve model performance.


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