📈 Linear Models

Linear models form the foundation of all supervised learning.
They are simple, elegant, and deeply interpretable — helping you understand how data, parameters, and predictions connect.
From predicting continuous outcomes to modeling probabilities, linear models teach you the core logic that drives all modern ML algorithms.

“All models are wrong, but some are useful.” — George Box

ℹ️
Interviewers use linear models to gauge your ability to reason about bias–variance trade-offs, optimization logic, and model interpretability.
If you can clearly explain Linear and Logistic Regression — how they learn, what assumptions they make, and how loss functions drive training — you demonstrate the depth needed for real ML system design discussions.
Key Skills You’ll Build by Mastering Linear Models
  • Analytical Thinking: Break complex patterns into linear relationships.
  • Optimization Intuition: Understand how parameters adjust through gradients.
  • Interpretability: Explain model weights and their real-world meanings.
  • Regularization Awareness: Control overfitting with mathematical precision.

🧮 Algorithms — The Heart of Linear Models

Linear models start with simple yet powerful algorithms that describe how inputs map to outputs.
They form the bridge between statistics and machine learning, teaching the logic of fitting, generalization, and prediction.

Linear Regression
Predict continuous outcomes using least squares estimation.
Logistic Regression
Model probabilities for binary classification tasks.

⚖️ Loss Functions — Measuring How Wrong We Are

Loss functions translate prediction errors into numbers that the model can minimize.
They define how learning happens — shaping how the model improves during training.

Mean Squared Error (MSE)
Primary loss for Linear Regression — penalizes large errors.
Mean Absolute Error (MAE)
Robust alternative less sensitive to outliers.
Huber Loss
Combines benefits of MSE and MAE for stable optimization.
Log Loss (Binary Cross-Entropy)
Loss function for binary logistic regression tasks.

🔧 Optimization — The Engine Behind Learning

Optimization methods adjust model parameters to minimize loss.
Understanding these methods gives you insight into how models actually learn from data — step by step, one gradient at a time.

Gradient Descent
The fundamental optimization algorithm that drives learning.

💡 Pro Tip:
Once you truly understand linear models, every advanced ML concept — from Support Vector Machines to Neural Networks — becomes far easier.
Think of this as the grammar of machine learning: master it once, and you’ll speak the language of algorithms fluently.