A-Z of Machine Learning and Computer Vision Terms
A
Activation Function
Active Learning
AI Agent
AI Assistants
AI Assisted Labeling
Algorithm
Anchor Boxes
Anomaly Detection
Artificial Intelligence (AI)
Attribute
B
Backpropagation
Backpropagation
Bagging
Batch
Batch Normalization
Bayesian Network
Bias
Big Data
Binary Classification
Blur
Boosting
Bounding Box
C
Calibration
Calibration Curve
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Chain of Thought (CoT)
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Clustering
COCO
Collaborative Filtering
Computer Vision
Computer Vision Model
Concept Drift
Conditional Random Field (CRF)
Confusion Matrix
Constrained Clustering
Contrastive Learning
Convolutional Neural Network (CNN)
Cross-entropy Loss
Cross-Validation
D
Data Approximation
Data Augmentation
Data Drift
Data Error
Data Mining
Data Operations
Data Pre-processing
Data Quality
Dataset
Decision Boundary
Decision List
Decision Stump
Decision Tree
Deep Learning
Deep Neural Networks
Deep Reinforcement Learning (DRL)
DICOM
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dynamic and Event-Based Classifications
E
Edge Cases
Edge Computing
Edge Detection
Elastic Net
Embedding Spaces
Ensemble Learning
Epoch
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
F
F1 Score
Factor Analysis
False Positive Rate
Feature
Feature Extraction
Feature Hashing
Feature Learning
Feature Scaling
Feature Selection
Feature Vector
Fisher’s Linear Discriminant
Foundation Models
FP-Growth Algorithm
Frame Rate
Frames Per Second (FPS)
Fully Connected Layer
Fuzzy Logic
G
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Data Pre-processing
Data pre-processing involves transforming raw data into a clean and structured format suitable for modeling. Real-world data is often incomplete, noisy, and inconsistent, so pre-processing includes tasks like data cleaning (handling missing values, smoothing noise, correcting errors), data integration (merging data from multiple sources), data transformation (normalization, encoding categorical variables, feature extraction), and data reduction (dimensionality reduction, sampling). For example, converting “yes/no” categories to 1/0, scaling features to [0,1] range, or extracting day of week from a timestamp are pre-processing steps. Effective pre-processing improves model performance and training speed, as many algorithms assume a certain well-behaved input format. It is a critical early phase in any data mining or machine learning project.
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