MACHINE LEARNING / OVERVIEW

Machine Learning — The Full Map

From raw data to predictions — the classical toolkit

EXPLANATION

Machine Learning is the art of building systems that learn patterns from data without being explicitly programmed. Unlike deep learning which learns representations, classical ML mostly uses hand-crafted features fed into mathematical models.

Three types of ML:
• Supervised   → learn from labeled data (X → y). Classification, regression
• Unsupervised → find structure in unlabeled data. Clustering, dimensionality reduction
• Reinforcement → agent learns by interacting with environment, maximizing reward

The ML workflow is always the same:
1. Data collection & cleaning
2. Feature engineering
3. Model selection
4. Training & hyperparameter tuning
5. Evaluation
6. Deployment

Classical ML is still widely used in production — not everything needs a neural network. Gradient boosting beats deep learning on tabular data. Logistic regression is still the go-to for interpretable classification.

DATA FLOW

RAW DATA
      ↓
  Preprocessing (scaling, encoding, imputation)
      ↓
  Feature Engineering (domain knowledge → useful features)
      ↓
  ┌──────────────────────────────────────────────┐
  │  Supervised        │  Unsupervised            │
  │  Linear Regression │  K-Means Clustering      │
  │  Logistic Reg      │  DBSCAN                  │
  │  Decision Trees    │  PCA                     │
  │  Random Forest     │  t-SNE / UMAP            │
  │  XGBoost / LGBM    │                          │
  │  SVM               │                          │
  └──────────────────────────────────────────────┘
      ↓
  Evaluation (accuracy, F1, AUC-ROC, RMSE...)
      ↓
  Deploy

CODE

BASH

1# The ML stack
2pip install scikit-learn numpy pandas matplotlib seaborn
3pip install xgboost lightgbm catboost
4pip install optuna                    # hyperparameter tuning
5
6# Verify
7python -c "import sklearn; print(sklearn.__version__)"

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