AI Foundations: Math & Python Essentials

It has likely been years since you last seen a derivative or manipulated a matrix. This intensive "Level-Zero" course is designed specifically for professionals who need to dust off their mathematical foundations while simultaneously building a python programming toolkit.

We don't teach math for the sake of theory; we teach it for the sake of implementation. This course provides a targeted "reactivation" of essential concepts, ranging from algebraic logic to linear algebra, re-contextualized through the lens of Machine Learning. By the end of this 16-hour journey, you will have bridged the gap between the equations of your past and the executable code of your future, ensuring you are fully AI-ready.

This course is part of the refresher and preparatory program designed to help you pass the entrance test and be ready for the AI Academy, but it can also be taken independently as a standalone introduction for beginners.

This course will cover the following topics:

1. Algebraic Logic & Foundations 

• Linear & Quadratic Equations

• Inequalities
• Function Evaluation
• Constant Function Graphs
• Prime numbers

2. Visual Calculus & Optimization

• Meaning of Derivatives
• Meaning of Integrals

3. Linear Algebra for Data 

• Basics of Vectors
• Basics of Matrices
• Matrix Operations

4. Python Foundations for Data

• Environment Setup
• Variables & Functions
• Array Manipulation
• Tables & Data Structures

5. Probability & Simulation 

• Basic Probability
• Independent Events
• Simulation Logic

6. Distributions & Descriptive Stats 

• Calculating and choosing between Mean, Median, and Mode.
• Spread & Variation
• Skewness
• Discrete vs. Continuous Distributions:

7. Correlation & Inferential Basics 

• Correlation Basic
• Sampling Basics

By the end of this course, you will be able to:

• Reactivate Quantitative Foundations: Solve linear and quadratic equations, evaluate functions, and interpret the geometry of inequalities and constant functions.
• Translate Math into ML Concepts: Explain the meaning of derivatives and integrals in the context of model optimization and understand the role of vectors and matrices in data representation.
• Build Python Fluency: Write functional Python code to handle basic arithmetic, evaluate functions, and manipulate data arrays.
• Compute Statistical Insights: Use Python to calculate mean, median, mode, variance, and skewness, while interpreting correlations and data distributions.
• Simulate Uncertainty: Model independent events and probability (such as coins and dice) using computational simulations rather than manual calculation.

Mainly through the lens of Machine Learning, focusing on practical application rather than abstract theory. As the course progresses, you will use your newly acquired Python skills to explore and solve topics like probability, distributions, and statistics. This hands-on approach ensures you can transform "rusty" math concepts into functional, executable code.