Do you want to truly understand how machine learning algorithms work under the hood? Whether you’re a data science enthusiast, ML engineer, or AI researcher, math is the key to mastering the field.
This course is your complete beginner-to-intermediate guide to the mathematical foundations of Machine Learning and AI. You’ll learn the core concepts with clear explanations, visualizations, and real-world examples, not dry theory.
By the end of this course, you’ll confidently understand and apply the math powering ML algorithms like linear regression, neural networks, decision trees, and more.
🧠 What You’ll Learn
✅ Build a strong foundation in Linear Algebra (vectors, matrices, dot products, eigenvalues)
✅ Understand Calculus used in optimization (derivatives, gradients, partial derivatives)
✅ Master Probability & Statistics for AI (Bayes’ Theorem, distributions, expectation, variance)
✅ Apply math to real ML algorithms: regression, classification, clustering, etc.
✅ Get hands-on with visual examples, problem sets, and Python demos
✅ Understand key math concepts behind Gradient Descent, Backpropagation, and Loss Functions
📚 Topics Covered
📐 Linear Algebra for ML
-
Vectors, Matrices, and Tensors
-
Matrix Multiplication & Properties
-
Eigenvalues, Eigenvectors, PCA
📊 Probability & Statistics
-
Basic Probability Rules & Bayes’ Theorem
-
Random Variables, Expectation, Variance
-
Gaussian, Bernoulli, and other distributions
-
Hypothesis Testing & Confidence Intervals
∂ Calculus & Optimization
-
Limits, Derivatives, and Chain Rule
-
Gradients, Partial Derivatives
-
Cost Functions and Gradient Descent
-
Backpropagation Intuition
🤖 Machine Learning Applications
-
Linear & Logistic Regression
-
Decision Trees and Entropy
-
Clustering with K-Means
-
Neural Networks Math Foundations
👨💻 Who This Course is For
-
Beginners entering AI, ML, or Data Science
-
Developers who struggle with the math behind ML algorithms
-
Students preparing for technical interviews or academic research
-
Anyone who wants to deeply understand how ML models work mathematically
✅ Course Prerequisites
-
High-school level math
-
Basic Python (for optional exercises – not required for understanding concepts)
-
No prior machine learning knowledge required
🔎 SEO Keywords (Embedded and Targeted)
-
Math for machine learning
-
Linear algebra for AI
-
Probability and statistics for ML
-
Calculus in machine learning
-
ML algorithm math
-
Mathematical foundations of AI
-
Backpropagation explained
-
Gradient descent tutorial
-
ML concepts for beginners
-
Learn ML theory
Lessons:
