Machine learning concepts
graph LR;
A[Machine Learning] --> B[Supervised Learning]
A --> C[Unsupervised Learning]
A --> D[Semi-Supervised Learning]
A --> E[Reinforcement Learning]
%% Supervised Learning
B --> B1[Classification]
B --> B2[Regression]
%% Unsupervised Learning
C --> C1[Clustering]
C --> C2[Dimensionality Reduction]
%% Classification Algorithms
B1 --> B1A[Logistic Regression]
B1A --> B1A_EXP{{"Predicts binary class probabilities"}}
B1 --> B1B[Decision Trees]
B1B --> B1B_EXP{{"Splits data using decision rules"}}
B1 --> B1C[SVM]
B1C --> B1C_EXP{{"Maximizes margin between classes"}}
B1 --> B1D[Neural Networks]
B1D --> B1D_EXP{{"Learns complex patterns with layers"}}
%% Regression Algorithms
B2 --> B2A[Linear Regression]
B2A --> B2A_EXP{{"Predicts continuous linear relationships"}}
B2 --> B2B[Multiple Linear Regression]
B2B --> B2B_EXP{{"Models multiple input features"}}
B2 --> B2C[Polynomial Regression]
B2C --> B2C_EXP{{"Fits nonlinear polynomial relationships"}}
B2 --> B2D[Ridge Regression]
B2D --> B2D_EXP{{"Linear regression with regularization"}}
B2 --> B2E[Time Series Forecasting]
B2E --> B2E_EXP{{"Predicts future values over time"}}
%% Clustering Algorithms
C1 --> C1A[K-Means]
C1A --> C1A_EXP{{"Partitions data into k clusters"}}
C1 --> C1B[Hierarchical Clustering]
C1B --> C1B_EXP{{"Builds tree-like cluster hierarchy"}}
C1 --> C1C[DBSCAN]
C1C --> C1C_EXP{{"Detects clusters based on density"}}
%% Dimensionality Reduction Techniques
C2 --> C2A[PCA]
C2A --> C2A_EXP{{"Reduces features by maximizing variance"}}
C2 --> C2B[t-SNE]
C2B --> C2B_EXP{{"Visualizes high-dimensional data in 2D/3D"}}
%% Semi-Supervised Learning
D --> D1[Semi-Supervised Learning]
D1 --> D1_EXP{{"Uses labeled and unlabeled data"}}
%% Reinforcement Learning
E --> E1[Reinforcement Learning]
E1 --> E1_EXP{{"Learning through rewards and penalties"}}
* Despite its name, logistic regression is used for binary classification or multi-class classification problems, not for regression tasks.
graph LR;
F[Important Concepts] --> F1[Supervised Learning]
F1 --> F1A[Feature Engineering]
F1 --> F1B[Overfitting/Underfitting]
F1 --> F1C[Cross-Validation]
F1 --> F1D[Bias-Variance Tradeoff]
F1 --> F1E[Regularization]
F --> F2[Unsupervised Learning]
F2 --> F2A[Clustering Evaluation: Silhouette Score]
F2 --> F2B[Dimensionality Reduction Benefits]
F2 --> F2C[Finding Hidden Patterns]
F --> F3[Semi-Supervised Learning]
F3 --> F3A[Use of Small Labeled Data]
F3 --> F3B[Leveraging Unlabeled Data]
F3 --> F3C[Practical Applications]
F --> F4[Reinforcement Learning]
F4 --> F4A[Reward/Penalty Systems]
F4 --> F4B[Exploration vs Exploitation]
F4 --> F4C[Markov Decision Processes: MDP]
%% Evaluation Metrics as a sub-branch of Supervised Learning
F1 --> G[Evaluation Metrics for Supervised Learning]
G --> G1[Classification: Accuracy, F1-score, ROC-AUC]
G --> G2[Regression: MSE, RMSE, R-squared]