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Data Science — Machine Learning Basics

Foundational ML concepts and terminology

M
ml_intern_lk 15 terms Feb 25, 2026
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Terms 15

1
Supervised Learning
Training model on labeled data; learns mapping from inputs to outputs
2
Unsupervised Learning
Finding patterns in unlabeled data; clustering, dimensionality reduction
3
Reinforcement Learning
Agent learns by receiving rewards/penalties for actions in an environment
4
Training Set
Data used to train the model
5
Validation Set
Data used to tune hyperparameters and evaluate during training
6
Test Set
Held-out data used to evaluate final model performance
7
Overfitting
Model performs well on training data but poorly on new data; too complex
8
Underfitting
Model too simple to capture patterns; performs poorly on training and test data
9
Feature
Individual measurable property used as input to a model
10
Label
Target variable or correct output the model tries to predict
11
Gradient Descent
Optimization algorithm iteratively adjusting weights to minimize loss
12
Loss Function
Measures difference between predicted and actual values; minimize during training
13
Neural Network
Model with layers of interconnected nodes; inspired by the brain
14
Hyperparameter
Setting configured before training; e.g. learning rate, number of layers
15
Cross-Validation
Technique splitting data multiple ways to ensure robust performance estimates