🔢 Top 10 Machine Learning Formulas Every Beginner Should Know

If you're diving into the world of machine learning, math can feel intimidating at first. But don’t worry — understanding the right formulas gives you superpowers.

In this post, we’ve compiled 10 must-know ML formulas, explained in plain text and practical terms. No fluff — just the core math that powers real-world machine learning.

🧠 Why These Formulas Matter

Whether you're building models, tuning hyperparameters, or understanding performance metrics, these formulas form the backbone of ML systems.

They help you:

Build and train models
Optimize performance
Evaluate results
Interpret predictions

✅ 10 Essential Machine Learning Formulas

1. Gradient Descent

Formula:
θ := θ - α ∇J(θ)
➡️ Updates model weights to minimize cost during training.

2. Linear Regression (Prediction Function)

Formula:
y = β₀ + β₁x
➡️ Predicts a continuous output based on input features.

3. Logistic Regression (Sigmoid Function)

Formula:
P(Y=1) = 1 / (1 + e^-(β₀ + β₁x))
➡️ Used for binary classification problems.

4. Loss Function – Mean Squared Error (MSE)

Formula:
MSE = Σ (yᵢ - ŷᵢ)² / n
➡️ Measures how far predictions deviate from actual values.

5. Cross-Entropy Loss (Classification)

Formula:
L = -[y log(ŷ) + (1 - y) log(1 - ŷ)]
➡️ Penalty for incorrect classification confidence.

6. Accuracy Score

Formula:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
➡️ Percentage of correct predictions in classification tasks.

7. Precision

Formula:
Precision = TP / (TP + FP)
➡️ What proportion of predicted positives are truly positive.

8. Recall

Formula:
Recall = TP / (TP + FN)
➡️ What proportion of actual positives were correctly identified.

9. F1 Score

Formula:
F1 = 2 * (Precision * Recall) / (Precision + Recall)
➡️ Balances precision and recall in imbalanced datasets.

10. Softmax Function

Formula:
Softmax(zᵢ) = e^(zᵢ) / Σ e^(zⱼ)
➡️ Converts output scores into probabilities for multi-class classification.

📌 Final Thoughts

These formulas power everything from spam filters to stock prediction engines. You don’t need to memorize all of them — just understand when and why to use them.

Top 10 Machine Learning Formulas for Beginners