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

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. 

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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