Machine Learning
Machine learning is a branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed. It encompasses supervised, unsupervised, and reinforcement learning approaches.
Understanding Machine Learning
Machine learning is the branch of artificial intelligence where systems learn patterns and make decisions from data without being explicitly programmed for every scenario. It encompasses supervised learning, where models learn from labeled examples; unsupervised learning, which discovers hidden patterns in unlabeled data; and reinforcement learning, where agents learn through trial and error with reward signals. The field has produced transformative applications across virtually every industry, from recommendation systems at Netflix and Spotify to medical diagnosis, fraud detection, and autonomous vehicles. Key concepts including neural networks, loss functions, gradient descent, and hyperparameter tuning form the foundation that practitioners must master. The modern machine learning pipeline involves data collection, annotation, feature engineering, model training, evaluation, and deployment through MLOps practices. Deep learning, a subset using multi-layered neural networks, has driven the most dramatic recent advances.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.