Learn #MachineLearning Coding Basics in a weekend – Glossary and Mindmap

Algorithms

Learn #MachineLearning Coding Basics in a weekend – Glossary and Mindmap Source – datasciencecentral.com

For background to this post, please see Learn #MachineLearning Coding Basics in a weekend. Here,we present the glossary that we use for the coding and the mindmap attached to these classes and upcoming book. 

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