A structured math bootcamp covering Linear Algebra, Statistics, Probability, and Calculus — everything you need to understand ML algorithms, neural networks, and data science workflows.
- Why Math for AI/ML/Data Science?
- What You Will Learn
- Topics Covered
- Applications in AI/ML/DS
- How to Use This Repository
Mathematics is the backbone of artificial intelligence, machine learning, and data science. It provides the foundational tools to:
- Build and train ML models (like neural networks, regression, classification)
- Analyze data using statistical methods
- Optimize algorithms and improve model performance
- Understand and interpret real-world datasets
Without a strong grasp of math, it's difficult to truly innovate, debug, or explain AI/ML systems.
- Essential mathematical concepts needed for AI, ML, and Data Science
- How these concepts are applied in real-world algorithms and workflows
- Practical problem-solving approaches for data-driven projects
- Vectors, matrices, eigenvalues, eigenvectors
- Applications in dimensionality reduction, neural networks, and data transformations
- Descriptive and inferential statistics
- Hypothesis testing, probability distributions
- Data analysis and decision-making
- Derivatives, rules of differentiation (product rule, chain rule)
- Applications in deep learning, optimization, and gradient-based algorithms
- Basic Probability: Events, sample spaces, axioms of probability, and fundamental rules
- Conditional Probability & Bayes’ Theorem: Understanding how probabilities update with new evidence
- Random Variables: Discrete and continuous types, probability mass and density functions
- Key Distributions: Bernoulli, Binomial, Poisson, Normal, and their use in modeling data
- Expectation & Variance: Calculating expected value and variance for random variables
- AI/ML Models: Math helps design algorithms like linear regression, logistic regression, neural networks, and support vector machines.
- Neural Networks: Calculus (especially gradients) is essential for backpropagation and training deep learning models.
- Data Science: Statistics is used for data cleaning, visualization, and hypothesis testing.
- Real-World Projects: These concepts are applied in recommendation systems, image recognition, natural language processing, and more.
- Probability Applications: Using probability for uncertainty modeling, risk assessment, and Bayesian inference in data science and ML.
- Browse the folders for detailed notes, explanations, and practical examples.
- Each folder covers a specific topic or application.
- Start with
0-What+We+Will+Learn.pdffor an overview of the content.
Explore, learn, and master the mathematics behind AI, ML, and Data Science!