Mathematical Foundation For Machine Learning and AI

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

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• Math for machine learning

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• Mathematical foundations of AI

• Backpropagation explained

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