Python, Math & Machine Learning Course

Course Website | YouTube | Telegram

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Course Structure

The course is organized into four major blocks, delivered as an interactive Quarto website.

1. Python (18 modules)

Core Python from first principles through advanced OOP and practical projects.

#	Topic
01	Introduction to Python
02	Conditions
03	Strings, Range, Lists
04	Loops
05	List/String Methods, Comprehensions
06	Tuples, Sets, Dictionaries
07	Functions I
08	Functions II
09	Files, Packages, Terminal
10	Git, Conda, PEP 8
11	Exception Handling
12	Streamlit & Recursion
13	Decorators
14	Classes
15	Inheritance & Polymorphism
16	Encapsulation & Abstraction
17	Dataclasses, Iterators, Generators, Context Managers
18	Project: YouTube Translator

2. Python Libraries (18 modules)

Real-world tools, data engineering, APIs, and software practices.

#	Topic
01	OpenAI API & Timestamps
02	NumPy
03	Pandas I
04	Pandas II
05	Data Analysis Project (Noble People)
06	Data Visualization
07	Project: Kargin
08	Logging & CLIs
09	Testing & Debugging
10	Web Scraping & Parallelization
11	Project: YSU Scraping
12	SQL
13	Pydantic
14	Miscellaneous Libraries
15	FastAPI
16	Databases & Supabase
17	Vibe Coding
18	Clean Code & Architecture

3. Mathematics (26 modules)

A rigorous yet intuition-first math curriculum, progressing from linear algebra through statistics. Each module is problem-centric with graded difficulty levels.

#	Area	Topic
00	Foundations	Sets, Combinatorics, Functions
01	Linear Algebra	Vectors, Norms, KNN
02	Linear Algebra	Matrices, Transformations
03	Linear Algebra	Linear Systems, Eigenvalues, Regression
04	Calculus	Limits, Continuity, Derivatives
05	Calculus	Extrema, Convexity, Taylor Series
06	Calculus	Integrals
07	Calculus	Multivariate Calculus, Gradient Descent
08	Optimization	Univariate (Golden Section, Brent's)
09	Optimization	Prerequisites & Gradient Descent
10	Optimization	Momentum & First-Order Methods
11	Optimization	Second-Order Methods
12	Optimization	Derivative-Free Methods
13	Optimization	Evolutionary Algorithms
14	Optimization	Bayesian Optimization
15	Optimization	Multi-Criteria Optimization
16	Probability	Basics, Bayes' Rule, Monty Hall
17	Probability	Expectation, Variance, Inequalities
18	Probability	Covariance & Correlation
19	Probability	Distributions (Discrete & Continuous)
20	Probability	Convergence, LLN, CLT
21	Statistics	Fundamentals
22	Statistics	Estimators
23	Statistics	MLE & MAP
24	Statistics	Confidence Intervals
25	Statistics	Hypothesis Testing

Accompanying Beamer slide decks (compiled with LaTeX/TikZ) are available in math/Lectures/ for both the optimization and statistics series.

4. Machine Learning (6 chapters)

Not started yet

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Repository Layout

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├── python/              # Python modules (Jupyter notebooks)
├── python_libs/         # Libraries & tools modules (Jupyter notebooks)
├── math/                # Math modules (Quarto .qmd files)
│   ├── Lectures/        # Beamer slide decks (.tex → .pdf)
│   │   ├── stat/        # Statistics lecture series
│   │   └── optim/       # Optimization lecture series
│   ├── Homeworks/       # Homework assignments (.pdf)
│   └── assets/          # Images, data files, helper notebooks
├── ml/                  # Machine Learning chapters
│   ├── Chapter 1–6/     # Lecture notes, code, homeworks per chapter
│   └── Datasets/        # Shared ML datasets
├── misc/                # Miscellaneous guides (Google Colab, etc.)
├── _quarto.yml          # Quarto book configuration
├── .github/workflows/   # GitHub Actions: auto-render & deploy on push
└── index.qmd            # Course landing page

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Getting Started

Viewing the course

The easiest way is to visit the course website, which renders all notebooks and Quarto documents as a searchable book with dark mode support.

Running locally

1. Clone the repository

   git clone https://github.com/HaykTarkhanyan/python_math_ml_course.git
   cd python_math_ml_course

2. Install dependencies

   • Python 3.10+
   • Quarto (for rendering .qmd files)
   • Jupyter / JupyterLab (for .ipynb notebooks)
   • TeX Live (for compiling Beamer slides)

3. Render the Quarto book (optional — the site auto-deploys via GitHub Actions on every push to main)

   quarto render

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Tech Stack

Tool	Purpose
Quarto	Book framework — renders .qmd and .ipynb into a unified website
Jupyter Notebooks	Python and Libraries modules
LaTeX / Beamer	Lecture slide decks with TikZ diagrams
GitHub Pages	Hosting the course website
GitHub Actions	Auto-render & deploy on every push to main (~5 min)

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Last updated: March 2026