The following entries (see below) are part of the glossary. The glossary is available as a PDF document. You can download it here.
Contents
Machine Learning concepts 4
- Learning Algorithm 4
- Predictive Model (Model) 4
- Model, Classification 4
- Model, Regression 4
- Representation Learning 4
- Supervised Learning 4
- Unsupervised Learning 4
- Semi-Supervised Learning 5
- Parameter 5
- Population 5
Algorithms 5
- Linear Regression 5
- Principal Component Analysis (PCA) 5
- K-Means 6
- Support Vector Machine (SVM) 7
- Transfer Learning 7
- Decision Tree 7
- Dimensionality Reduction 8
- Instance based learning 8
- Instance-Based Learning 8
- K Nearest Neighbors 8
- Kernel 9
Training: Basics 9
- Training 9
- Training Example 9
- Training Set 9
- Iteration 9
- Convergence 9
Training: Data 10
- Standardization 10
- Holdout Set 10
- Normalization 10
- One-Hot Encoding 10
- Outlier 11
- Embedding 11
Regression 12
- Regression 12
- Regression Algorithm 12
- Regression Model 12
Classification 12
- Classification 12
- Class 12
- Hyperplane 12
- Decision Boundary 12
- False Negative (FN) 13
- False Positive (FP) 13
- True Negative (TN) 13
- True Positive (TP) 13
- Precision 13
- Recall 14
- F1 Score 14
- Few-Shot Learning 14
- Hinge Loss 14
- Log Loss 14
Ensemble 15
- Ensemble 15
- Ensemble Learning 15
- Strong Classifier 15
- Weak Classifier 15
- Boosting 15
Evaluation 15
- Validation Example 15
- Validation Loss 15
- Validation Set 16
- Variance 16
- Cost Function 16
- Cross-Validation 16
- Overfitting 16
- Regularization 16
- Underfitting 16
- Evaluation Metrics 17
- Evaluation Metric 17
- Regression metrics 17
- Mean Absolute Error. 17
- Mean Squared Error. 17
- R^2 17
- Classification metrics 17
- Accuracy. 17
- Logarithmic Loss. 17
- Area Under ROC Curve. 17
- Confusion Matrix. 17
- Hyperparameter 18
- Hyperparameter 18
- Hyperparameter Tuning 18
- Grid Search 18
- Random Search 18
Original post: https://www.datasciencecentral.com/profiles/blogs/learn-machinelearning-coding-basics-in-a-weekend-glossary-and?s=09