A
- A/B Testing
- Accuracy
- Activation Function
- Adam Optimizer
- AIOps (AI for IT Operations)
- Algorithm
- Anomaly Detection
- API (Application Programming Interface)
- Artificial Intelligence (AI)
- AUC (Area Under the Curve)
- Autoregressive Model
B
- Backpropagation
- Bag of Words (BoW)
- Bag-of-Characters
- Bag-of-N-Grams
- Bagging (Bootstrap Aggregating)
- Batch Normalisation
- Batch Size
- Bayesian Optimisation
- BERT (Bidirectional Encoder Representations from Transformers)
- Bias
- Bias Mitigation
- Bias-Variance Tradeoff
- Big Data
- Binning
- BLEU Score
- Boosting
- Box Plot
- Business Intelligence (BI)
C
- CatBoost
- Causation
- CI/CD (Continuous Integration / Continuous Deployment)
- Classification
- Clustering
- Confounding Variable
- Confusion Matrix
- Correlation
- Cosine Similarity
- Cost Function
- Cross-Entropy Loss
- Cross-Validation
- Curse of Dimensionality
D
- Dashboard
- Data Analyst
- Data Cleaning
- Data Engineer
- Data Ethics
- Data Governance
- Data Lake
- Data Lineage
- Data Mart
- Data Pipeline
- Data Provenance
- Data Scientist
- Data Set
- Data type
- Data Visualisation
- Data Warehouse
- Data Wrangling
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Decision Tree
- Deep Learning
- Dendrogram
- Differential Privacy
- Dimensionality Reduction
- Dropout
- Dummy Variable
E
- Early Stopping
- Elastic Net
- Elbow Method
- Embedding Layer
- Ensemble Learning
- Ensemble Model
- Epoch
- Epoch vs Iteration
- ETL (Extract, Transform, Load)
- Euclidean Distance
- Explainable AI (XAI)
F
- F1 Score
- Fairness in AI
- Feature
- Feature Engineering
- Feature Importance
- Feature Scaling
- Feature Selection
- Feature Store
- Federated Learning
- Few-Shot Learning
- Fine-Tuning
- Forecasting
G
H
I
J
K
L
- L1 Regularisation (Lasso)
- L2 Regularisation (Ridge)
- Label
- Label Encoding
- Language Model
- Learning Curve
- Learning Rate
- Lemma vs Stem
- Lemmatization
- LightGBM
- LIME (Local Interpretable Model-agnostic Explanations)
- Linear Regression
- Log Transformation
- Logistic Regression
- Loss Function
M
- Machine Learning
- Manhattan Distance
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- MLOps (Machine Learning Operations)
- MNIST dataset
- Model Deployment
- Model Drift
- Model Evaluation
- Model Monitoring
- Model Registry
- Model Tuning
- Multicollinearity
N
- Naive Bayes
- Named Entity Recognition (NER)
- Natural Language Processing (NLP)
- Neural Network
- Noise
- Normalization
O
P
- Parameter
- Part-of-Speech Tagging (POS Tagging)
- Perplexity
- Positional Encoding
- Precision
- Predictive Analytics
- Pretrained Model
- Principal Component Analysis (PCA)
- Prompt Engineering
R
- Random Forest
- Random Search
- Recall (Sensitivity)
- Regression Analysis
- Regularisation
- ReLU (Rectified Linear Unit)
- RMSprop
- RoBERTa
- ROC Curve (Receiver Operating Characteristic Curve)
- Root Mean Squared Error (RMSE)
S
- Scatter Plot
- Self-Attention
- Sentiment Analysis
- SGD (Stochastic Gradient Descent)
- SHAP (Shapley Additive Explanations)
- Sigmoid Function
- Signal
- Silhouette Score
- SMOTE (Synthetic Minority Over-sampling Technique)
- Stacking
- Standardization
- Stemming
- Stop Words
- Supervised Learning
- Support Vector Machine (SVM)
- Synthetic Data
T
- T5 (Text-to-Text Transfer Transformer)
- Tanh Function
- Test Data
- Test Error
- Test Set
- TF-IDF (Term Frequency–Inverse Document Frequency)
- Time Series
- Token
- Token Limit
- Tokenization
- Training Data
- Training Error
- Training Set
- Transfer Learning
- Transformer Model
U
V
W
X
Z