Machine Learning Notes 2026
Comprehensive ML concepts, algorithms, mathematics, and implementation notes. Perfect for study reference and interview preparation.
Machine Learning Basics
Machine Learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
Types of Machine Learning
Supervised Learning
Learning with labeled data. The algorithm learns from training data and makes predictions.
Examples: Classification, Regression
Unsupervised Learning
Learning with unlabeled data. The algorithm finds patterns and relationships.
Examples: Clustering, Dimensionality Reduction
Reinforcement Learning
Learning through interaction with environment. Agent learns by receiving rewards/punishments.
Examples: Game AI, Robotics
Supervised Learning Algorithms
Linear Regression
Predicts continuous values. Finds the best-fit line through data points.
Use Case: House price prediction, Sales forecasting
Logistic Regression
Used for binary classification. Outputs probability between 0 and 1.
Use Case: Spam detection, Disease diagnosis
Algorithm Comparison
| Algorithm | Type | Best For | Complexity |
|---|---|---|---|
| Linear Regression | Regression | Linear relationships | Low |
| Logistic Regression | Classification | Binary classification | Low |
| Decision Trees | Both | Interpretable models | Medium |
| Random Forest | Both | High accuracy | High |
| SVM | Classification | Clear margin separation | Medium-High |
Deep Learning Concepts
Neural Network Architecture
Deep Learning uses artificial neural networks with multiple layers (deep networks).
CNN
Convolutional Neural Networks
Specialized for grid-like data (images). Uses convolutional layers.
Applications: Image classification, Object detection
RNN
Recurrent Neural Networks
Designed for sequential data. Has memory of previous inputs.
Applications: Time series, Natural language
Transformer
Transformer Architecture
Uses attention mechanism. Parallel processing of sequences.
Applications: LLMs, Translation
ML Quick Reference Cheatsheet
Evaluation Metrics
Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
Regression: MSE, RMSE, MAE, R² Score
Common Libraries
scikit-learn: Traditional ML algorithms
TensorFlow/PyTorch: Deep Learning frameworks
Pandas/NumPy: Data manipulation
Data Preprocessing
• Handling missing values
• Feature scaling (Standardization/Normalization)
• Encoding categorical variables
• Train-test split (80-20 or 70-30)
Download Complete ML Notes
Get the complete Machine Learning notes as PDF for offline study and reference.
