Mathematics for AI/DS
Build rock-solid mathematical foundations for artificial intelligence and data science. Our character-driven approach makes complex mathematical concepts intuitive and immediately applicable to real-world AI problems.
Core Mathematical Topics
- Linear Algebra: Vectors, matrices, eigenvalues, and transformations
- Calculus: Derivatives, gradients, and optimization fundamentals
- Statistics & Probability: Distributions, Bayes’ theorem, and statistical inference
- Discrete Mathematics: Graph theory, combinatorics, and logic
- Optimization: Gradient descent, constrained optimization, and convex analysis
- Information Theory: Entropy, mutual information, and coding theory
AI/ML Applications
- Principal Component Analysis (PCA): Dimensionality reduction with eigenvalues
- Singular Value Decomposition (SVD): Matrix factorization for recommender systems
- Gradient Descent: The optimization engine behind neural networks
- Backpropagation: How calculus powers deep learning training
- Bayesian Methods: Probabilistic approaches to machine learning
- Support Vector Machines: Geometric interpretation of classification
Learning Philosophy
- Intuition First: Understand the “why” before the “how”
- Visual Learning: Geometric interpretations and interactive visualizations
- Practical Examples: Every concept tied to real AI applications
- Progressive Complexity: From basic concepts to advanced theorems
- Python Implementation: See math in action with working code
- Story-Driven: Follow relatable characters on their mathematical journey
Featured Learning Paths
- ML Mathematics Bootcamp: Essential math for machine learning practitioners
- Deep Learning Mathematics: Calculus and linear algebra for neural networks
- Statistics for Data Science: Probability and inference for data analysis
- Optimization for AI: Mathematical optimization techniques in AI systems
Transform abstract mathematical concepts into practical AI superpowers with our unique blend of storytelling, visualization, and hands-on implementation.
