Should You Learn Math First or Alongside ML?

"Do I need to learn Math first to start Data Science or Machine Learning (ML)?" "Can I learn ML without a strong Math background?" "Math is so vast—what topics should I focus on?"

Sazit Suvo

Designer & Editor

Learn Relevant Math While Learning ML

Math is a helpful tool in your ML learning journey, but you don’t need to tackle an extensive Math syllabus upfront. Instead, learn relevant Math topics as they come up while studying ML algorithms.

For example:

When learning Linear Regression, focus on Linear Algebra and Basic Calculus.
For Naive Bayes, dive into Probability concepts.

This approach allows you to simultaneously gain Math skills and hands-on problem-solving expertise with machine learning.

Essential Math Topics for Machine Learning

Linear Algebra

Why it’s needed: For data representation, model training, and algorithm optimization.
Key concepts to learn:

Vectors, Matrices, Matrix Multiplication
Eigenvalues & Eigenvectors
Singular Value Decomposition (SVD)

Calculus

Why it’s needed: For optimizing models using Gradient Descent.
Key concepts to learn:

Derivatives, Partial Derivatives
Chain Rule
Optimization (Maxima & Minima)

Probability & Statistics

Why it’s needed: To understand model decision-making and prediction.
Key concepts to learn:

Conditional Probability
Bayes’ Theorem
Probability Distributions (Normal, Binomial)
Hypothesis Testing

Linear Optimization & Cost Functions

Why it’s needed: For improving loss functions and algorithm performance.

Discrete Mathematics

Why it’s needed: For algorithms like Decision Trees and Random Forest.

5 Steps to Learn Math While Learning ML

1️⃣ Choose an Algorithm: Begin by understanding the ML algorithm you want to study.
2️⃣ Learn Relevant Math Topics: Focus only on the Math concepts related to that algorithm.
3️⃣ Practice: Apply formulas with pen and paper.
4️⃣ Learn Through Code: Reinforce theoretical concepts by coding them in Python.
5️⃣ Use Resources: Leverage quality resources for easy and detailed explanations.

Your Next Steps

Start learning Python.
Explore libraries like Pandas, NumPy, and Matplotlib.
Learn foundational ML algorithms (e.g., Linear Regression, Decision Tree, Boosting, PCA) alongside relevant Math topics.
Progress to Neural Networks.
Master core Generative AI algorithms.
Practice with small, hands-on projects.

The myth that you need to learn Math extensively before starting ML is false. Begin your journey by focusing on practical hands-on experience with ML/DL, and the Math concepts will naturally fall into place as you progress.

Avoid anyone who discourages you with rigid Math-first advice. Instead, dive in and let your practice guide your understanding!

Do You Need to Learn Math First to Start Data Science & Machine Learning?

Should You Learn Math First or Alongside ML?

Learn Relevant Math While Learning ML