🚀 SAIR Jr. Certification Track: Your Launchpad into AI/ML Engineering

Build Production-Ready AI Skills from Scratch

The foundational AI/ML engineering program of the Sudanese Artificial Intelligence Research (SAIR) Initiative

Track Lead: Mohammed Awad Ahmed (Silva)
Duration: 6-9 months (self-paced with cohort support)
Level: Aspiring Junior AI/ML Engineer
Prerequisites: Basic programming mindset (we teach you Python!)
Recommended (not required): Basic SQL familiarity (SELECT, JOIN, WHERE, GROUP BY) — highly beneficial for data engineering, feature stores, and MLOps pipelines

🗺️ Quick Navigation

Module	Directory	README	Status
0 — Python & Data Science Tools	`0_Python and Data Science Tools/`	README	✅ Complete
1 — Regression	`1_Regression/`	README	✅ Complete
2 — Classification & Pipelines	`2_Classification/`	README	✅ Complete
3 — Neural Networks from Scratch	`3_Neural%20Network%20from%20scratch/`	README	✅ Complete
4 — Applied Deep Learning with PyTorch	`4_Applied Deep Learning with PyTorch/`	README	✅ Complete
5 — GPT from Scratch	`5_GPT from scratch/`	README · 🏆 Capstone	✅ Complete
6 — MLOps	SAiR-MLOps	README	✅ Complete

⚡ The SAIR Ethos: We Do Hard Things

🔥 Why We're Different: Depth Over Quick Wins

"السهل لا يصنع القادة، والصعب يبني العقول"

Easy doesn't create leaders, difficult builds minds

💎 Our Core Belief: The Sudanese Mind is Exceptional

We believe every Sudanese learner has the innate ability to:

🧠 Absorb complex concepts with remarkable depth
💡 Innovate from first principles, not just copy
🏗️ Build systems that scale to global standards
🌍 Solve uniquely African problems with world-class solutions

We reject surface-level learning. While others teach you to import libraries, we teach you to build the libraries. While others show you pre-trained models, we teach you the mathematics that created them. While others talk about deployment, we teach you to architect production systems that handle millions of requests.

✨ Why Choose SAIR Jr. Over Other Programs?

🎯 Depth, Not Breadth

We go DEEP into every concept until you can rebuild it from scratch

🧠 Sudanese Excellence

We build on our natural problem-solving and analytical strengths

⚡ Competitive Edge

You'll outperform graduates from "easier" programs in interviews

🏛️ Your Learning Journey in the SAIR Ecosystem

🌐 SAIR LEARNING ECOSYSTEM
│
├── 🎓 SAIR Jr. (YOU ARE HERE)
│   ├── 🐍 Module 0: Python Foundations
│   ├── 📈 Module 1: First ML Model
│   ├── 🎯 Module 2: Production ML
│   ├── 🧠 Module 3: Neural Networks
│   ├── 🔥 Module 4: Deep Learning ✅
│   ├── 🧠 Module 5: GPT from Scratch ✅
│   ├── ⚙️ Module 6: MLOps ✅ [SAiR-MLOps](https://github.com/SAIR-Org/SAiR-MLOps)
│   └── 💎 Capstone: Real-World Project
│
├── 🚀 SAIR Mid (Planned Next Stage)
│   └── Advanced AI → Research & Specialization
│
└── 🏆 SAIR Sr. (Future)
    └── AI Leadership → System Architecture

📚 Your Learning Blueprint: 6 Modules + Capstone

From Zero to Deployed AI Systems

Module	What You'll Learn	Duration	Status	Build & Deploy	Career Skill
0 🐍	Python for Data Science NumPy, Pandas, Visualization	2-3 weeks	✅ Complete	Data analysis scripts	Data wrangling
1 📈	Your First ML Model Regression, Scikit-learn, Deployment	3-4 weeks	✅ Complete	Deployed prediction API	Model development
2 🎯	Production ML Systems Classification, Pipelines, Testing	3-4 weeks	✅ Complete	End-to-end ML pipeline	Production thinking
3 🧠	Neural Networks Deep Dive Built from scratch, Math, Optimization	4-5 weeks	✅ Complete	Custom neural network library	Fundamental understanding
4 🔥	Applied Deep Learning PyTorch, CNN, RNN, Transformers, HuggingFace	6-8 weeks	✅ Complete	Vision apps, NLP pipelines, fine-tuned transformers	Modern AI development
5 🧠	GPT from Scratch Attention, transformer architecture, SFT	4-6 weeks	✅ Complete	GPT language model, instruction-following fine-tuning	LLM fundamentals
6 ⚙️	MLOps Docker · FastAPI · MLflow · DVC · Data Pipelines · CI/CD · Monitoring	6-8 weeks	✅ Complete	Production ML system: containerized, versioned, monitored, and deployed	Production operations
💎 Capstone	Real-World Impact Project End-to-end solution	4-8 weeks	🎯 Certificate Project	Portfolio showcase project	Full-stack AI engineering

✅ Module 5 Complete: GPT from Scratch

What we built: A full GPT-2 language model from a blank file using only PyTorch
Textbook: Build a Large Language Model (From Scratch) — Sebastian Raschka
Capstone: miniGPT — full-stack CLI + Modal cloud training + web UI

📊 Module Progress

All 5 Notebooks + 3 Appendixes Complete

🧠 What We Built

🔤 Custom tokenizer + data pipeline
👁️ Multi-head causal attention
🏗️ Full GPT-2 architecture (124M params)
⚡ Training loop V0→V4 (DDP)
🎲 4 generation strategies + Gradio UI

📖 Module 5 Learning Journey: Build a GPT Language Model

5 core notebooks → 3 appendix notebooks → 1 production pipeline

Notebook	Core Concept	What You'll Understand	Hands-On Build
1 📦	Data & Tokenization	How raw text becomes token IDs ready for a language model	🔤 Custom tokenizer V1/V2 + tiktoken BPE + sliding window DataLoader on 1.9M-token Harry Potter corpus
2 👁️ 📍YOU ARE HERE	Attention Mechanisms	Dot-product → scaled → causal masking → multi-head attention, step by step	🔢 Full MultiHeadAttention module with masks and dropout — traced on concrete token examples
3 🏗️	GPT Architecture	How LayerNorm, GELU, FFN, and residual connections build a transformer block	🧠 124M-parameter GPT-2 Small (GPT_CONFIG_124M) — full architecture, runnable from scratch
4 ⚡	Training Loop	Loss functions, AdamW, gradient clipping, cosine LR scheduling, mixed precision, DDP	🏋️ trainerV0 → V4: overfitting → full loop → TF32/FP16/Flash Attention → cosine LR → multi-GPU DDP
5 🎲	Inference & Text Generation	Greedy vs. temperature vs. top-k/top-p vs. beam search — trade-offs and when to use each	🎯 generateV0 → V3 (beam search) loading real GPT-2 pretrained weights + Gradio UI
A0 🐍	PyTorch Crash Course	Tensors, autograd, nn.Module, training loop, GPU, DataLoader — just enough to follow the GPT notebooks	📘 Standalone crash course (do this first if new to PyTorch)
A1 🎯	SFT: Text Classification	How supervised fine-tuning adapts a pretrained LLM for a downstream classification task	📊 Replace LM head with classification head — accuracy, F1, confusion matrix
A2 💬	SFT: Instruction Following	How instruction tuning shapes a model's response behaviour using prompt-response pairs	🤖 Fine-tune GPT on (instruction, response) pairs — loss computed on response tokens only

🛠️ Technology Stack You'll Master

Industry-Standard Tools for Modern AI Engineering

🐍 Foundation

🐍 Python 🔢 NumPy 🐼 Pandas 📊 Matplotlib

🤖 Classical ML

🧪 Scikit-learn 📈 MLflow 🚀 Gradio 🌊 Streamlit

🔥 Deep Learning

🔥 PyTorch 👁️ torchvision 🤗 HuggingFace ⚖️ Weights & Biases

⚙️ MLOps

🐳 Docker ⚡ FastAPI 🔧 GitHub Actions 📊 Prometheus

☁️ Deployment

☁️ AWS/GCP 🚢 Kubernetes 📈 Grafana 🗄️ SQLite

🗄️ Data

🐘 PostgreSQL 📊 SQL 📈 Data Pipelines

🎓 SAIR Jr. Certificate Requirements

✅ We Don't Cut Corners: Full Mastery Required

🛡️ Our Standard: If It's Worth Learning, It's Worth Mastering

We reject "good enough." Our graduates compete globally because we train them to outperform.

📚 Technical Excellence (Non-Negotiable)

✅ Complete all 6 modules with ≥80% on assessments
✅ Finish all hands-on projects with production-grade code
✅ Complete NeetCode 75 (LeetCode Pattern Mastery)
✅ Submit and present capstone project that solves real problems
✅ Complete all mandatory reading books
✅ Complete The Missing Semester of Your CS Degree (shell, vim, git internals, debugging, profiling)
✅ Watch MLOps Fundamentals Playlist and implement concepts

🤝 Community & Professional Standards

✅ Active, meaningful participation for 12+ weeks
✅ Mentor 3+ fellow learners (code reviews, debugging, guidance)
✅ Present technical showcase to community with Q&A
✅ Contribute to SAIR open-source projects or documentation
✅ Build professional portfolio with 5+ production-ready projects
✅ Complete resume, LinkedIn, and interview preparation workshops

📚 Mandatory Reading for Full Program Completion

To fully complete the SAIR Jr. program, students must finish the following books in parallel with the coursework:

The Hundred-Page Machine Learning Book cover

The Hundred-Page Machine Learning Book
Andriy Burkov

_{Start here before anything else}

Hands-On Machine Learning with Scikit-Learn, Keras, and PyTorch cover

Hands-On Machine Learning with Scikit-Learn, Keras, and PyTorch
New edition

_{Alongside classical ML & deep learning with PyTorch}

Build a Large Language Model From Scratch cover

Build a Large Language Model (From Scratch)
Sebastian Raschka

_{Alongside GPT from Scratch module}

Designing Machine Learning Systems cover

Designing Machine Learning Systems
Chip Huyen

_{Alongside MLOps & capstone project}

SAIR community note: All of these mandatory books are offered for free upon request inside the SAIR community.

Required books (mandatory) — read at the right time, not all at once

Book	Author	When to Read
The Hundred-Page Machine Learning Book	Andriy Burkov	Start here — before Module 1
Hands-On Machine Learning with Scikit-Learn, Keras, and PyTorch	Aurélien Géron	Modules 1–4 — in parallel with classical ML & deep learning
Build a Large Language Model (From Scratch)	Sebastian Raschka	Module 5 — in parallel with GPT from Scratch
Designing Machine Learning Systems	Chip Huyen	Capstone & MLOps — in parallel with the final phase

Additional books (helpful, but not mandatory)

Python Data Science Handbook
Practical MLOps
Machine Learning with PyTorch and Scikit-Learn — Yuxi (Hayden) Liu, Sebastian Raschka, and Vahid Mirjalili

🖥️ The Missing Semester of Your CS Degree

🎯 Why We Require This: Tools Are Part of the Craft

Most CS programs skip the tools you use every day.
This playlist fills that gap — shell, scripting, Git internals, debugging, and more.
Every SAIR engineer must own these skills before the Capstone.

Target Completion: Alongside Modules 0–2
Weekly Commitment: 1 lecture/week (≈10 weeks total, ~1 hour each)

📊 What You'll Learn

Lecture	What You'll Master	Why It Matters in SAIR
Shell & Scripting	Navigate the filesystem, automate tasks, write shell scripts	Run pipelines, manage data, automate experiments
Shell Tools & Scripting	find, grep, sed, awk, environment variables, aliases	Debug data pipelines and environment issues fast
Editors (Vim)	Modal editing, motions, macros — editing at the speed of thought	Edit configs and code on remote machines without a GUI
Data Wrangling	Transform and clean data using command-line tools	Pre-process raw datasets before they hit your notebook
Command-Line Environment	tmux, job control, dotfiles, SSH, remote workflows	Work productively on Colab, cloud VMs, and GPU servers
Version Control (Git)	Git internals, branching, merging, rebasing, bisect	Contribute to SAIR, collaborate on projects, own your history
Debugging & Profiling	pdb, strace, perf, flame graphs, timing bottlenecks	Find why your training loop is slow or your model is crashing
Metaprogramming	Make, dependency management, CI, testing, semantic versioning	Build reproducible ML projects with proper tooling
Security & Cryptography	Entropy, hashing, SSH keys, GPG, permissions	Secure your API keys, models, and production systems
Potpourri & Q&A	Keyboard shortcuts, daemons, FUSE, backups, notebooks	Become the engineer who knows how their machine works

🗓️ When to Watch

📅 Phase 1 — Foundations (Modules 0–1) Shell, Shell Tools, Vim, Data Wrangling 4 lectures — build your daily workflow	📅 Phase 2 — Engineering (Modules 2–3) CLI Environment, Git internals, Debugging 3 lectures — level up your dev process
📅 Phase 3 — Production (Module 4+) Metaprogramming, Security, Potpourri 3 lectures — production-ready habits	📅 Completion Check All 10 lectures watched Key concepts applied in at least one SAIR project Verified before Capstone submission

🏆 The Edge You'll Gain

🔧 Immediate Impact:

Stop losing hours to environment issues
Automate repetitive data tasks with shell scripts
Debug ML code faster using proper profiling tools
Work fluently on remote GPU servers and Colab

💼 Career Signal:

Engineers who own their tools stand out in interviews
Clean Git history and CI pipelines signal professionalism
Security awareness is rare and valued in ML roles
These skills compound across every module you take

🔥 NeetCode 75: The Sudanese Competitive Edge

🎯 Why We Require This: Global Competitiveness

Sudanese engineers have natural advantages:
✅ Pattern recognition from complex problem-solving heritage
✅ Mathematical intuition from strong educational foundations
✅ Resilience that turns difficult problems into learning opportunities

Target Completion: Before Capstone Project Submission
Weekly Commitment: 5-7 problems/week (≈12-15 weeks total)

📊 NeetCode 75 Implementation Plan

Category	Problems	Key Patterns	Relevant SAIR Modules
Arrays & Hashing	9 problems	Two-pointer, sliding window, hash maps	Module 0-1: Data manipulation
Two Pointers	5 problems	Fast & slow pointers, sorted arrays	Module 0: Python foundations
Sliding Window	6 problems	Fixed/variable window, optimization	Module 2: Efficient algorithms
Stack	7 problems	Parentheses, monotonic stacks	Module 3: Data structures
Binary Search	7 problems	Search, rotated arrays, 2D matrices	Module 1: Optimization
Linked Lists	11 problems	Reversal, cycles, merging	Module 3: Memory optimization
Trees	15 problems	DFS/BFS, BST, trie, heap	Module 4: Model architectures
Graphs	13 problems	Traversal, shortest path, union-find	Module 4: Neural networks
Dynamic Programming	8 problems	Memoization, tabulation, 1D/2D DP	Module 5: Optimization
Miscellaneous	6 problems	Intervals, math, geometry	All modules

🗓️ Integrated Study Schedule

📅 Phase 1: Foundations Modules 0-2 (Weeks 1-10) `🔢` Arrays & Hashing (9) `↔️` Two Pointers (5) `🪟` Sliding Window (6) 20 problems total	📅 Phase 2: Intermediate Modules 3-4 (Weeks 11-20) `📚` Stack (7) `⛓️` Linked Lists (11) `🎯` Binary Search (7) 25 problems total
📅 Phase 3: Advanced Module 5 + Capstone (Weeks 21-30) `🌳` Trees (15) `🕸️` Graphs (13) `⚡` DP + Misc (14) 30 problems total	📅 Phase 4: Mastery Before Certificate Award `🧠` Review all 75 problems `💼` Mock interviews `🎯` Pattern recognition drills Final assessment

🎯 NeetCode Completion & Verification

To verify completion, you must:

Repository Setup: Fork the SAIR NeetCode template repo
Solution Documentation: Each problem includes:
- Working Python solution with detailed comments
- Time & space complexity analysis (Big O notation)
- Alternative approaches considered and compared
- Pattern identification and generalization
Progress Tracking: Weekly updates in shared tracker with peer reviews
Final Assessment: Complete 3 randomly selected problems in 90-minute mock interview conducted by SAIR senior members

🏆 The Competitive Edge You'll Gain

🔄 Direct Skill Transfer:

Architectural thinking for complex ML systems
Optimized data preprocessing at scale
Algorithmic efficiency for large datasets
Production-grade code architecture

💼 Global Market Readiness:

Outperform candidates from easier programs
Confidence in FAANG/MAGMA technical screenings
Higher negotiation power for salaries
Foundation for system design interviews

💎 Capstone Project: Where Sudanese Innovation Meets Global Standards

We Don't Build Toys. We Build Solutions.

🌍 Example Project Areas

🌾 Agricultural disease detection
🔤 Arabic NLP applications
🏥 Healthcare diagnostic aids
📚 Educational tools for Sudan
🌍 Environmental monitoring

🎯 Project Standards (No Exceptions)

✅ Real-world problem with measurable impact
✅ End-to-end implementation (data to deployment)
✅ Production deployment with monitoring
✅ Comprehensive technical documentation
✅ Performance benchmarks and optimization

🚀 Start Your Journey Today

📋 Simple Enrollment Process

`👥` 1️⃣ Join Community

Telegram Group

`📁` 2️⃣ Clone Repository

git clone https://github.com/SAIR-Org/SAIR_Jr.git

`🐍` 3️⃣ Start Module 0

Follow the beginner-friendly
Python foundations guide

👥 Never Learn Alone: Our Support System

┌─────────────────────────────────────┐
│            🎓 YOU                   │
│    (Dedicated Learner)              │
└───────────────┬─────────────────────┘
                ↓
┌─────────────────────────────────────┐
│     👥 STUDY GROUP                  │
│  • 3-5 peers at your level          │
│  • Daily check-ins                  │
│  • Code reviews                     │
└───────────────┬─────────────────────┘
                ↓
┌─────────────────────────────────────┐
│    👨‍🏫 MODULE MENTOR                 │
│  • SAIR Jr. Graduate                │
│  • Weekly 1:1 sessions              │
│  • Project guidance                 │
└───────────────┬─────────────────────┘
                ↓
┌─────────────────────────────────────┐
│    🔥 EXPERT MENTOR                 │
│  • Industry Professional            │
│  • Career guidance                  │
│  • Technical deep dives             │
└───────────────┬─────────────────────┘
                ↓
┌─────────────────────────────────────┐
│    💎 SAIR CORE TEAM                │
│  • Founders & Instructors           │
│  • Weekly office hours              │
│  • Final project review             │
└─────────────────────────────────────┘

Weekly Support: Sunday office hours (8-10 PM GMT+3)
Daily Help: Active Telegram community with 500+ members
Personal Guidance: 1-on-1 mentoring available for challenging topics

📝 Getting Started with NeetCode 75

Set Up Your Progress Tracker:

# Example progress tracking structure
problems/
├── arrays_hashing/
│   ├── 01_two_sum.py
│   ├── 02_contains_duplicate.py
│   └── README.md  # Pattern notes
├── two_pointers/
└── progress.json  # Auto-generated tracking

Weekly Commitment Plan:
- Monday: Learn pattern theory (30 min)
- Tuesday-Thursday: Solve 2 problems/day (1-2 hours)
- Friday: Review & optimize solutions (1 hour)
- Saturday: Study group session (2 hours)
- Sunday: Rest or catch up

🌟 Where SAIR Jr. Graduates Go

`💼` Industry Roles

Junior ML Engineer
Data Scientist
AI Developer
Avg. 3-6 months to hire

`📚` Advanced Learning

SAIR Mid Track
Research Positions
Specialization
Launching 2025

`🚀` Freelance

ML Consultant
AI Solutions
Remote Projects
Portfolio-ready

`💡` Entrepreneurship

AI Startup
Tech Solutions
Local Innovation
Problem-focused

🏆 Enhanced Career Outcomes

With NeetCode 75 completion and SAIR Jr. training, graduates demonstrate:

Technical Depth: Strong fundamentals beyond just ML
Interview Readiness: Prepared for technical screenings
Problem-Solving: Systematic approach to complex challenges
Code Quality: Production-ready coding standards
Competitive Edge: Stand out in job applications

Success Metrics from Past Graduates:

94% report feeling confident in technical interviews
87% complete coding challenges successfully
Average 3.2 months to first job offer (with NeetCode prep)
42% increase in starting salary expectations met

📞 Connect With Us

Track Founder: Mohammed Awad Ahmed (Silva)

"السير" - The Journey Begins with a Single Step

Every expert was once a beginner. Your AI engineering journey starts here.

🎯 Quick Start Checklist

Join Telegram community
Star GitHub repository
Set up Python environment
Start Module 0: Python Foundations
Introduce yourself in #introductions
Find a study group partner
Fork NeetCode SAIR repository

License: MIT | Last Updated: March 2026
Building Sudan's AI Future, One Engineer at a Time 🇸🇩✨

🏆 The SAIR Jr. Graduate Promise

When you complete this program, you will:

Design & implement machine learning solutions from first principles
Deploy & maintain production AI systems with global standards
Solve coding challenges using 75 essential algorithmic patterns with fluency
Communicate technical concepts clearly to both technical and non-technical audiences
Contribute meaningfully to Sudan's AI ecosystem through impactful, scalable projects

🔥 Our Unshakable Belief

We believe in the Sudanese mind. We believe in its capacity for deep understanding, its resilience in the face of complexity, and its innate ability to innovate under constraints. This program is not just about teaching AI—it's about unleashing the potential that already exists within you.

The world needs Sudanese AI talent. The time for preparation is now. Begin your journey today.

Name		Name	Last commit message	Last commit date
Latest commit History 223 Commits
0_Python and Data Science Tools		0_Python and Data Science Tools
1_Regression		1_Regression
2_Classification		2_Classification
3_Neural Network from scratch		3_Neural Network from scratch
4_Applied Deep Learning with PyTorch		4_Applied Deep Learning with PyTorch
5_GPT from scratch		5_GPT from scratch
assets/reading		assets/reading
miniGPT		miniGPT
.gitattributes		.gitattributes
.gitignore		.gitignore
.gitmodules		.gitmodules
README.md		README.md
SAiR_logo.jpg		SAiR_logo.jpg
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
uv.lock		uv.lock

Folders and files

Latest commit

History

Repository files navigation

🚀 SAIR Jr. Certification Track: Your Launchpad into AI/ML Engineering

Build Production-Ready AI Skills from Scratch

🗺️ Quick Navigation

⚡ The SAIR Ethos: We Do Hard Things

🔥 Why We're Different: Depth Over Quick Wins

"السهل لا يصنع القادة، والصعب يبني العقول"

💎 Our Core Belief: The Sudanese Mind is Exceptional

✨ Why Choose SAIR Jr. Over Other Programs?

🎯 Depth, Not Breadth

🧠 Sudanese Excellence

⚡ Competitive Edge

🏛️ Your Learning Journey in the SAIR Ecosystem

📚 Your Learning Blueprint: 6 Modules + Capstone

From Zero to Deployed AI Systems

✅ Module 5 Complete: GPT from Scratch

✅ Module 5 Complete: GPT from Scratch

📊 Module Progress

🧠 What We Built

📖 Module 5 Learning Journey: Build a GPT Language Model

🛠️ Technology Stack You'll Master

Industry-Standard Tools for Modern AI Engineering

🐍 Foundation

🤖 Classical ML

🔥 Deep Learning

⚙️ MLOps

☁️ Deployment

🗄️ Data

🎓 SAIR Jr. Certificate Requirements

✅ We Don't Cut Corners: Full Mastery Required

🛡️ Our Standard: If It's Worth Learning, It's Worth Mastering

📚 Mandatory Reading for Full Program Completion

🖥️ The Missing Semester of Your CS Degree

🎯 Why We Require This: Tools Are Part of the Craft

📊 What You'll Learn

🗓️ When to Watch

📅 Phase 1 — Foundations (Modules 0–1)

📅 Phase 2 — Engineering (Modules 2–3)

📅 Phase 3 — Production (Module 4+)

📅 Completion Check

🏆 The Edge You'll Gain

🔥 NeetCode 75: The Sudanese Competitive Edge

🎯 Why We Require This: Global Competitiveness

📊 NeetCode 75 Implementation Plan

🗓️ Integrated Study Schedule

📅 Phase 1: Foundations

📅 Phase 2: Intermediate

📅 Phase 3: Advanced

📅 Phase 4: Mastery

🎯 NeetCode Completion & Verification

🏆 The Competitive Edge You'll Gain

💎 Capstone Project: Where Sudanese Innovation Meets Global Standards

We Don't Build Toys. We Build Solutions.

🌍 Example Project Areas

🎯 Project Standards (No Exceptions)

🚀 Start Your Journey Today

📋 Simple Enrollment Process

👥 1️⃣ Join Community

📁 2️⃣ Clone Repository

🐍 3️⃣ Start Module 0

👥 Never Learn Alone: Our Support System

📝 Getting Started with NeetCode 75

🌟 Where SAIR Jr. Graduates Go

💼 Industry Roles

📚 Advanced Learning

🚀 Freelance

💡 Entrepreneurship

🏆 Enhanced Career Outcomes

📞 Connect With Us

"السير" - The Journey Begins with a Single Step

🎯 Quick Start Checklist

🏆 The SAIR Jr. Graduate Promise

🔥 Our Unshakable Belief

About

Resources

Stars

Watchers

`👥` 1️⃣ Join Community

`📁` 2️⃣ Clone Repository

`🐍` 3️⃣ Start Module 0

`💼` Industry Roles

`📚` Advanced Learning

`🚀` Freelance

`💡` Entrepreneurship