Fundamentals of AI / ML / Data Science is designed for anyone who wants to truly understand how AI works under the hood — Learning by first principles, not just use libraries. You’ll learn the mathematical foundations — linear algebra, calculus, probability, and statistics — that power machine learning. These concepts are introduced gradually and intuitively to ensure deep understanding and long-term retention.

The course then builds your understanding of core AI paradigms: supervised, unsupervised, and reinforcement learning. You’ll study essential algorithms like linear and logistic regression, Naive Bayes, k-means, decision trees, and introduction to neural networks.

By the end, you’ll be able to read papers, follow advanced tutorials, and build a solid base for deep learning, generative AI, or agentic AI systems.

Perfect for thought leaders, developers, data scientists, or anyone serious about mastering the why behind AI.

Unit 1: Mathematical Foundations for AI

1. Linear Algebra

   1. Vectors, matrices, matrix operations

   2. Eigenvalues & eigenvectors

   3. Singular Value Decomposition (SVD)

2. Differential Calculus

   1. Derivatives & partial derivatives

   2. Chain rule

   3. Gradient & gradient descent

   4. Optimization basics

3. Probability & Statistics

   1. Random variables & distributions

   2. Mean, variance, standard deviation

   3. Bayes’ theorem

   4. Conditional probability

   5. Hypothesis testing & confidence intervals

Unit 2: Machine Learning Paradigms

1. Supervised Learning Basics

   1. Concept of labeled data

   2. Bias-variance tradeoff

   3. Overfitting & regularization

2. Unsupervised Learning Basics

   1. Concept of unlabeled data

   2. Clustering vs. dimensionality reduction

3. Reinforcement Learning Intro

   1. Agent-environment loop

   2. Rewards, states, actions

   3. Value functions & policies

   4. Exploration vs. exploitation

Unit 3: Core Algorithms

1. Regression Algorithms

   1. Simple & multiple linear regression

   2. Cost functions, gradients

2. Classification Algorithms

   1. Logistic regression for classification

   2. Naive Bayes classifier

   3. Decision Trees & Entropy

   4. K-Nearest Neighbors

3. Clustering & Dimensionality Reduction

   1. K-means clustering

   2. Hierarchical clustering (overview)

   3. Principal Component Analysis (PCA)

4. Intro to Neural Networks

   1. Perceptron & activation functions

   2. Single hidden layer networks

   3. Backpropagation (high-level view)

Unit 4: Putting It All Together

1. Evaluation & Validation

   1. Cross-validation

   2. Confusion matrix, accuracy, precision, recall

   3. ROC curves & AUC

2. Capstone Project

   1. Apply your math and algorithms to a real dataset

   2. Build, train, and evaluate models step by step

   3. Present results & insights