Education Playground

Contents

Education Playground#

Python License Jupyter Book Status Last Updated Version

Learn Python, AI, and Computing — From Zero to Hero

A comprehensive, self-paced learning platform covering Python, AI/ML, GPU Computing, and Computer Science for all skill levels. 100% free and runs in your browser!

🌐 Live Site: https://mykolas-perevicius.github.io/Education_Playground/

📋 Project Documentation#

New to this project? Start here:

🚀 START LEARNING (30 seconds)#

New here? An interactive guide will help you find your path when you visit the site!

Or jump straight in:

🌟 Complete Beginner?#

→ Start Lesson 1 (Colab) Opens in your browser • No installation needed • Works on any device

💪 Have Some Experience?#

→ Take the 5-Minute Level Finder Find your perfect starting point

🚀 Advanced User?#

→ Browse All Topics | → GPU Computing | → AI/ML

✨ Why Education Playground?#

  • ✅ No Setup Required — Start learning in your browser with Google Colab

  • ✅ Complete Curriculum — 40+ interactive notebooks covering beginner to expert

  • ✅ Self-Paced — Learn at your own speed with clear progress tracking

  • ✅ Real Projects — Build actual applications, not just exercises

  • ✅ Free Forever — Open source and accessible to everyone

  • ✅ Mobile Friendly — Learn on phone, tablet, or desktop

🆕 Recent Major Enhancements (2025)#

All notebooks have been significantly expanded with:

  • ✅ 3-6x content expansion across all difficulty levels

  • ✅ Production-quality code with detailed explanations

  • ✅ Real-world applications and use cases

  • ✅ Comprehensive exercises with difficulty ratings

  • ✅ Self-check quizzes with detailed explanations

  • ✅ Pro tips, common mistakes, and debugging guides

  • ✅ NEW: Performance Computing - Profiling, optimization, Numba, multiprocessing

  • ✅ NEW: CUDA & GPU Computing - Parallel computing, CuPy, PyTorch GPU, multi-GPU

Overview#

This repository contains a complete curriculum organized into three difficulty levels. Each level includes modules on:

  • Python Programming: Core language skills

  • AI & Machine Learning: From basics to deep learning

  • Computing Fundamentals: How computers work and CS theory

All lessons within each level can be completed in parallel - choose your own path based on your interests!

Getting Started#

Path A: Complete Beginner (Never Coded Before)#

Start here if you’ve NEVER programmed before!

  1. Beginner Scripts Track (beginner_scripts/)

    • Simple Python scripts with extensive comments

    • 10 lessons covering absolute basics

    • Each lesson: 15-30 minutes

    • Run with: python beginner_scripts/1_hello_world.py

    • Complete this first! Then proceed to Path B.

Path B: Some Programming Experience#

  1. Take the Calibration Test

Start by running the calibration test to determine your skill level:

jupyter notebook 00_calibration_test.ipynb

The test covers:

  • Basic syntax and variables

  • Control flow and functions

  • Data structures

  • Object-oriented programming

  • Advanced concepts (decorators, generators, algorithms)

Based on your score, you’ll be recommended one of three levels:

  • 0-30 points: Easy Level

  • 31-60 points: Medium Level

  • 61+ points: Hard Level

2. Choose Your Path#

Course Structure#

The curriculum is organized into difficulty levels (Beginner Scripts, Easy, Medium, Hard) plus an essential Developer Tools track that complements all levels.

🌱 Beginner Scripts Track (Pre-Easy)#

For absolute beginners who have never programmed before!

📂 Location: beginner_scripts/

10 Python script lessons covering the fundamentals:

  1. Hello World - Your first program

  2. Variables and Types - Storing information

  3. String Manipulation - Working with text

  4. Basic Math - Numbers and operations

  5. Lists and Dictionaries - Organizing data

  6. List/Dict Methods - Advanced operations

  7. Imports - Using modules

  8. Error Handling - Dealing with mistakes

  9. Indexing and For Loops - Iteration

  10. Conditionals and While Loops - Control flow

Features:

  • Simple .py scripts (no notebooks required)

  • Extensive comments explaining every line

  • TODO exercises for practice

  • Time estimates for each section

  • Instructor-friendly format

Total Time: 3-5 hours for complete beginners

After completing: Progress to Easy Level notebooks or take the Calibration Test!

Core Tracks by Difficulty#

Each level contains three parallel tracks that can be completed in any order:

Easy Level (Beginner)#

Perfect for those new to programming. Lessons can be done in any order, though starting with Python basics is recommended.

Python Fundamentals Track#

  1. Introduction to Python (easy/01_introduction_to_python.ipynb)

    • Hello World and basic output

    • Running Python code

    • Your first programs

  2. Variables and Data Types (easy/02_variables_and_data_types.ipynb)

    • Creating and using variables

    • Strings, integers, floats, and booleans

    • Type checking

  3. Basic Operations and Conditionals (easy/03_basic_operations_and_conditionals.ipynb)

    • Mathematical operations

    • Comparison operators

    • If/else statements

AI & ML Introduction Track#

  1. Introduction to AI and Machine Learning (easy/04_intro_to_ai_and_ml.ipynb)

    • What is AI and ML?

    • Simple pattern recognition

    • Rule-based AI systems

    • Basic recommendation systems

Computing Fundamentals Track#

  1. Computing Fundamentals (easy/05_computing_fundamentals.ipynb)

    • How computers store information

    • Binary numbers and data representation

    • ASCII and text encoding

    • Bits, bytes, and storage units

Medium Level (Intermediate)#

For those with basic programming knowledge. All lessons are standalone and can be completed in any order.

Python Advanced Track#

  1. Functions and Modules (medium/01_functions_and_modules.ipynb)

    • Defining functions

    • Parameters and return values

    • Importing and using modules

    • Scope and namespaces

  2. Data Structures (medium/02_data_structures.ipynb)

    • Lists and list operations

    • Dictionaries for key-value storage

    • Sets and tuples

    • List comprehensions

  3. Classes and Object-Oriented Programming (medium/03_classes_and_oop.ipynb)

    • Creating classes and objects

    • Instance variables and methods

    • Constructors (__init__)

    • Basic inheritance

Machine Learning Track#

  1. Machine Learning Basics (medium/04_machine_learning_basics.ipynb)

    • Introduction to scikit-learn

    • Supervised vs unsupervised learning

    • Classification and regression

    • Model evaluation and accuracy

  2. Data Analysis with Pandas (medium/05_data_analysis_with_pandas.ipynb)

    • Working with DataFrames

    • Data cleaning and manipulation

    • Grouping and aggregation

    • Basic data visualization

Algorithms & Problem Solving Track#

  1. Algorithms and Problem Solving (medium/06_algorithms_and_problem_solving.ipynb)

    • Searching algorithms (linear, binary)

    • Sorting algorithms (bubble, selection)

    • Time complexity basics

    • Problem-solving strategies

Hard Level (Advanced)#

Advanced topics for experienced programmers. Lessons are independent and more demanding - dive deeper into areas that interest you!

Advanced Python Track#

  1. Advanced Functions and Decorators (hard/01_advanced_functions_and_decorators.ipynb)

    • Higher-order functions and closures

    • Lambda functions and functional programming

    • Creating and using decorators

    • Advanced decorator patterns

  2. Generators and Iterators (hard/02_generators_and_iterators.ipynb)

    • Iterator protocol

    • Generator functions and yield

    • Generator expressions

    • Coroutines and data pipelines

  3. Algorithms and Complexity Analysis (hard/03_algorithms_and_complexity.ipynb)

    • Big O notation

    • Sorting algorithms (quicksort, merge sort)

    • Searching algorithms

    • Dynamic programming

    • Data structures (heaps, graphs, trees)

Deep Learning & AI Track#

  1. Deep Learning and Neural Networks (hard/04_deep_learning_and_neural_networks.ipynb)

    • Neural network architecture

    • Building networks with TensorFlow/Keras

    • Convolutional Neural Networks (CNNs)

    • Transfer learning

    • Advanced optimization techniques

  2. Advanced ML and Natural Language Processing (hard/05_advanced_ml_and_nlp.ipynb)

    • Ensemble learning (Random Forest, XGBoost)

    • Natural Language Processing

    • Text classification and sentiment analysis

    • Word embeddings

    • Hyperparameter tuning

Computer Systems Track#

  1. Computer Systems and Theory (hard/06_computer_systems_and_theory.ipynb)

    • Computer architecture (Von Neumann, CPU simulators)

    • Memory hierarchy and cache replacement (LRU, LFU, FIFO)

    • Concurrency, parallelism, and synchronization

    • Computational complexity theory (P vs NP, TSP)

    • Automata theory and finite state machines

    • Virtual memory, paging, and TLB

  2. Project Ideas & Implementation Guide (hard/07_project_ideas.ipynb) 📐

    • 10+ Production-Ready Project Blueprints:

      • Build Your Own Web Framework (WSGI, routing, middleware)

      • Distributed Task Queue (Celery-like system)

      • Image Recognition ML Pipeline (end-to-end)

      • Programming Language Interpreter (lexer, parser, AST)

    • Complete starter code (300-400 lines per project)

    • Architecture diagrams and system design

    • Project planning framework (MVP, testing, deployment)

    • Production best practices checklist

High-Performance Computing Track 🚀#

  1. Performance Computing (hard/10_performance_computing.ipynb) ⚡ NEW!

    • Profiling & Optimization: cProfile, line_profiler, finding bottlenecks

    • Memory Optimization: Generators, slots, memory profiling

    • NumPy Vectorization: 10-100x speedups with proper vectorization

    • Numba JIT Compilation: C-speed Python with @jit decorator

    • Multiprocessing & Threading: Bypass GIL, parallel processing

    • Async I/O: asyncio for high-concurrency applications

    • Cython: C extensions for maximum performance

    • Real-world optimization case studies

  2. CUDA & GPU Parallel Computing (hard/11_cuda_and_parallel_computing.ipynb) 🎮 NEW!

    • GPU Architecture: Understanding CUDA programming model

    • CuPy: NumPy for GPUs (10-100x faster array operations)

    • PyTorch GPU: Deep learning acceleration

    • Parallel Algorithms: Map, reduce, scan, stencil patterns

    • GPU Memory Management: Optimization techniques

    • Multi-GPU Programming: Data parallelism, model parallelism

    • Real Applications: Image processing, Monte Carlo, deep learning

    • Performance optimization and profiling

Classic Problems & Interview Prep Track 🔥#

  1. Classic Problems Collection (hard/08_classic_problems.ipynb) ⭐

  • LeetCode Classics: Two Sum, Three Sum, Longest Substring, Merge K Lists, Trapping Rain Water

  • Dynamic Programming: Knapsack, Longest Common Subsequence, Edit Distance

  • Graph Algorithms: Dijkstra’s Shortest Path, Cycle Detection

  • Cryptography: Caesar Cipher, Vigenère Cipher, RSA Implementation

  • ML from Scratch: K-Nearest Neighbors, Linear Regression, Neural Networks

  • Interview patterns: Hash maps, two pointers, sliding window, heap, DP

  1. CTF Challenges - Hacker Training (hard/09_ctf_challenges.ipynb) 🚩

  • Web Exploitation: SQL Injection, XSS, security vulnerabilities

  • Binary Exploitation (Pwn): Buffer overflows, format string attacks

  • Reverse Engineering: Decompiling, crackmes, understanding binaries

  • Cryptography: Breaking encryption, classical and modern ciphers

  • Forensics: Steganography, file carving, metadata extraction

  • OSINT: Open Source Intelligence, finding hidden information

  • Misc: Encoding puzzles, CTF techniques and tools

  • Prepares for: Bug bounties, penetration testing, security careers

💡 These problems are legendary - solved by generations of programmers. Master them for:

  • FAANG interviews (Google, Facebook, Amazon, Apple, Netflix)

  • Competitive programming (Codeforces, TopCoder)

  • Cybersecurity careers and CTF competitions

  • High-performance computing and GPU acceleration

  • Real-world problem-solving skills

📊 Hard Track Statistics:

  • 11 comprehensive notebooks (was 9, added 2 NEW!)

  • 539KB total content (was 370KB, +169KB of new material)

  • Average 49KB per notebook (vs 14KB originally)

  • All notebooks enhanced to production quality

  • 50+ hours of learning material

Developer Tools Track (The Missing Semester)#

Essential skills for every developer - Inspired by MIT’s “The Missing Semester of Your CS Education”. These lessons can be taken at any level!

📂 Location: tools/

Core Tools#

  1. Shell and Command Line (tools/01_shell_basics.ipynb)

    • Terminal fundamentals and navigation

    • File operations and text processing

    • Pipes, redirection, and command chaining

    • Essential for all developers

  2. Shell Scripting (tools/02_shell_scripting.ipynb)

    • Writing bash scripts

    • Automating repetitive tasks

    • Text processing with sed, awk, grep

  3. Version Control with Git (tools/03_git_essentials.ipynb)

    • Git fundamentals and workflows

    • Branching, merging, and collaboration

    • Industry-standard version control

  4. Text Editors (tools/04_text_editors.ipynb)

    • Vim basics and power features

    • VS Code productivity tips

    • Editor customization

  5. Data Wrangling (tools/05_data_wrangling.ipynb)

    • Processing data with Unix tools

    • Regular expressions

    • JSON/CSV manipulation

  6. Debugging and Profiling (tools/06_debugging_profiling.ipynb)

    • Python debugger (pdb)

    • Performance profiling and optimization

    • Static analysis tools

  7. Security Essentials (tools/07_security_essentials.ipynb)

    • SSH and authentication

    • Secrets management

    • Common vulnerabilities

  8. Build Systems and CI/CD (tools/08_build_and_automation.ipynb)

    • Makefiles and automation

    • GitHub Actions and CI/CD

    • Docker basics

  9. Package Management (tools/09_package_management.ipynb)

    • pip, poetry, and virtual environments

    • Dependency management

    • System package managers

  10. Dotfiles and Configuration (tools/10_dotfiles.ipynb)

    • Shell configuration (.bashrc, .zshrc)

    • Tool configuration

    • Dotfile management

💡 Recommended: Start with Shell Basics and Git Essentials - you’ll use these every day!

See tools/README.md for detailed information and learning paths.

Installation#

📖 For detailed setup instructions, see SETUP.md

Quick Start#

  1. Clone the repository:

git clone https://github.com/mykolas-perevicius/Education_Playground.git
cd Education_Playground

  1. Install dependencies:

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install all packages
pip install -r requirements.txt

  1. Start learning:

jupyter notebook
# Then open 00_calibration_test.ipynb

Need help? Check SETUP.md for:

  • Platform-specific instructions (Windows, macOS, Linux)

  • Alternative installation methods (Conda, Docker)

  • GPU setup for deep learning

  • Troubleshooting common issues

How to Use This Course#

  1. Start with the calibration test to find your level

  2. Choose lessons based on your interests - within each level, lessons are designed to be completed in parallel

  3. Complete all exercises in each notebook

  4. Practice regularly - coding and ML skills improve with practice

  5. Experiment freely - modify examples and see what happens

  6. Build projects - combine skills from different tracks

  7. Move to the next level when you feel comfortable

Flexible Learning Paths#

Path 1: Python-First Approach#

Focus on Python fundamentals before exploring AI and systems

Path 2: AI-Focused Approach#

Start with AI/ML concepts alongside Python basics

Path 3: Systems-Focused Approach#

Learn computing fundamentals and algorithms

Path 4: Balanced Approach#

Rotate between tracks for well-rounded knowledge

The choice is yours! All paths lead to comprehensive understanding.

Features#

  • Self-paced learning: Go at your own speed

  • Interactive exercises: Learn by doing

  • Real code examples: See concepts in action

  • Progressive difficulty: Build skills systematically

  • Parallel learning tracks: Choose your own adventure

  • Comprehensive coverage: From basics to cutting-edge topics

  • Practical applications: Real-world examples throughout

Learning Tips#

  • Run every code cell: Don’t just read - execute and experiment!

  • Modify examples: Change values and see what happens

  • Complete exercises: They reinforce what you’ve learned

  • Take breaks: Your brain needs time to process new information

  • Mix tracks: Don’t feel locked into one track - explore!

  • Review previous lessons: Repetition strengthens understanding

  • Build projects: Combine concepts from multiple lessons

  • Join communities: Share your progress and learn from others

What’s Included#

Python Programming#

  • Variables, data types, control flow

  • Functions, classes, OOP

  • Advanced patterns (decorators, generators)

  • Data structures and algorithms

  • Performance optimization and profiling

  • Memory management and efficiency

AI & Machine Learning#

  • Introduction to AI concepts

  • Machine learning with scikit-learn

  • Deep learning with TensorFlow/Keras/PyTorch

  • Natural Language Processing and transformers

  • Model evaluation, optimization, and hyperparameter tuning

  • GPU-accelerated deep learning

  • End-to-end ML pipelines and MLOps

High-Performance & Parallel Computing ⚡ NEW!#

  • Performance profiling (cProfile, line_profiler, memory_profiler)

  • Optimization techniques (vectorization, JIT compilation, caching)

  • NumPy vectorization for 10-100x speedups

  • Numba for C-speed Python

  • Multiprocessing and threading for parallelism

  • Async I/O with asyncio

  • GPU/CUDA programming with CuPy and PyTorch

  • Multi-GPU training and data parallelism

  • Parallel algorithm patterns and optimization

Computing Fundamentals#

  • Binary and data representation

  • Computer architecture and CPU simulators

  • Memory hierarchy and cache systems

  • Concurrency, parallelism, and synchronization

  • Computational complexity (P vs NP)

  • Systems programming and virtual memory

  • GPU architecture and CUDA programming

Supplementary Materials#

📚 RESOURCES.md - Comprehensive external learning resources

  • Curated free courses from top platforms (Coursera, edX, Fast.ai)

  • Interactive learning platforms (Futurecoder, LearnPython.org, Kaggle)

  • Books, YouTube channels, podcasts, communities

  • Learning paths organized by focus area

⚡ PYTHON_CHEATSHEET.md - Quick Python reference

  • All core concepts in one place

  • Code examples for every feature

  • Best practices and common pitfalls

  • One-liners and performance tips

🤖 ML_AI_CHEATSHEET.md - ML/AI quick reference

  • scikit-learn complete guide

  • TensorFlow/Keras essentials

  • NLP and text processing

  • Model deployment patterns

  • Troubleshooting common issues

🚀 Project Ideas (in each level folder)

  • Easy: 10 beginner projects (calculator, games, chatbot)

  • Medium: 10 intermediate projects (ML apps, data analysis, algorithms)

  • Hard: 10+ advanced projects (frameworks, compilers, production ML systems)

  • All include features, bonuses, and learning goals

Contributing#

Found a typo or have a suggestion? Contributions are welcome!

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/improvement)

  3. Commit your changes (git commit -m 'Add some improvement')

  4. Push to the branch (git push origin feature/improvement)

  5. Open a Pull Request

License#

This project is open source and available under the MIT License.

Support#

If you have questions or need help:

  • Open an issue in this repository

  • Check existing issues for solutions

  • Review the lesson materials carefully

Project Statistics#

Content Overview#

  • Total Notebooks: 40+ interactive Jupyter notebooks

  • Total Content: 2MB+ of educational material

  • Code Examples: 1,000+ working code samples

  • Exercises: 200+ hands-on exercises with solutions

  • Quizzes: 100+ self-check questions

  • Learning Hours: 200-300 hours of comprehensive material

Track Breakdown#

Track

Notebooks

Size

Key Topics

Beginner Scripts

10

50KB

Python fundamentals

Easy Level

5

200KB

Intro to programming & AI

Medium Level

6

260KB

Intermediate Python & ML

Hard Level

11

539KB

Advanced topics + GPU computing

Developer Tools

10

350KB

Professional dev skills

Recent Enhancements (2025)#

  • ✅ Hard notebooks 01-07: Expanded from 14KB to 54KB average (3.8x growth)

  • ✅ Notebook 04 (Deep Learning): 13KB → 56K (4.3x expansion)

  • ✅ Notebook 05 (ML/NLP): 14KB → 48KB (3.4x expansion)

  • ✅ Notebook 06 (Systems): 19KB → 55KB (2.9x expansion)

  • ✅ Notebook 07 (Projects): 15KB → 87KB (5.8x expansion!)

  • ✅ NEW Notebook 10: Performance Computing (40KB)

  • ✅ NEW Notebook 11: CUDA & GPU Programming (38KB)

  • Total enhancement: +200KB of production-quality content

Quality Improvements#

  • Before: Basic examples, minimal exercises

  • After: Production code, comprehensive exercises, quizzes, pro tips

  • Code Quality: Professional-grade with detailed comments

  • Real-World Focus: Industry-applicable examples

  • Testing: All code validated and tested

💬 Need Help?#

Stuck on something? You’re not alone!

💭 Communities#

🐛 Report Issues#

📚 Additional Learning Resources#

Want to supplement your learning? Check out these excellent resources:

Python Fundamentals#

Machine Learning & AI#

Practice & Challenges#

Developer Tools#

Video Tutorials#

Acknowledgments#

Created to make programming, AI, computer science, and high-performance computing education accessible and interactive for everyone.

Special thanks to the open-source community and the thousands of developers who have contributed to the tools and libraries featured in this curriculum.

🎓 Start Your Journey#

Ready to begin? Click here to start learning!

Happy Learning! Remember: every expert was once a beginner. Whether you’re interested in building AI models, optimizing code for maximum performance, programming GPUs, understanding how computers work at the silicon level, or becoming a Python master - your journey starts here.

From Hello World to CUDA kernels, from basic loops to distributed systems, from simple scripts to production ML pipelines - we’ve got you covered!

Keep practicing, stay curious, and enjoy the journey! 🚀

“The only way to learn a new programming language is by writing programs in it.” - Dennis Ritchie

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