Beginner
What is machine learning?
Machine Learning (ML) is a subfield of artificial intelligence in which systems learn patterns from data or experience to make predictions, classifications, recommendations, or decisions without being explicitly programmed for every case.
What is supervised learning?
Supervised learning is a machine learning approach in which a model learns from examples that pair inputs with known target outputs or labels. The model uses those labeled examples to learn mappings that can support prediction, classification, or regression on new data.
How is unsupervised learning different from supervised learning?
Use Supervised Learning when labeled input-output examples exist; use Unsupervised Learning when the goal is to discover structure without labels.
What is reinforcement learning?
Reinforcement Learning (RL) is a machine learning paradigm in which an agent learns to act in an environment by receiving rewards or penalties for its actions. The objective is to learn a policy that maximizes expected cumulative reward over time.
Intermediate
What is self-supervised learning?
Self-supervised learning is a machine learning approach that creates training signals from unlabeled data itself. It converts aspects of an unsupervised problem into supervised learning by generating surrogate labels, making it useful when labeled data is scarce but unlabeled data is abundant.
What is semi-supervised learning?
Semi-supervised learning is a machine learning approach that uses both labeled and unlabeled data during training. It is useful when labeled examples are costly or limited but larger volumes of unlabeled data can help improve representation, classification, or prediction.
What is training data?
Training data is the dataset used to teach or fit a machine learning model by adjusting its learnable parameters. Its quality, quantity, diversity, labeling, and provenance strongly influence the performance, reliability, and compliance posture of the resulting model.
What is overfitting?
Overfitting occurs when a model learns the training data too closely, including noise or idiosyncratic details, and performs poorly on new data. It is commonly indicated by high training performance but weaker validation or test performance.
What is underfitting?
Underfitting occurs when a model is too simple or insufficiently trained to capture meaningful patterns in the data. An underfit model performs poorly on both training data and new data because it has not learned the underlying relationships needed for the task.
What is a decision tree?
A decision tree is an interpretable supervised learning model that represents decision rules in a tree-like structure. Inference follows a path from a root node through feature-based conditions to a leaf node that provides the predicted class or value.
Advanced
What is deep learning?
Deep learning is a subfield of machine learning that uses neural networks with multiple layers to learn representations and patterns from data.
How are classification and regression different?
Use Classification when the target is a category; use Regression when the target is a continuous or numerical value.