I have a Master's degree in Information Technology and a keen interest in artificial intelligence and deep learning.

Passionate about applying cutting edge AI to address real world issues and drive meaningful change. Seeking opportunities to further develop my abilities and contribute value through data driven solutions.

Your database is probably a mess and costing you money.

Understanding the 6 Normal Forms can transform your database from chaos to clarity

🎯 Quick breakdown:

1NF: Stop storing "Math, English, History" in one cell Split it into separate rows

2NF: Don't make non-key data depend on part of your primary key Create focused relationship tables

3NF: Eliminate those sneaky indirect dependencies Department location shouldn't live in employee records

BCNF: Make every determinant a superkey No exceptions to the dependency rules

4NF: One table, one purpose Don't mix student courses with student hobbies

5NF: Perfect reconstruction through joins only The ultimate normalized state
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Muhammad Usman Shahbaz

Normalization_in_SQL

The Python Cheat Sheets Every Developer Should Bookmark.

Python is simple… until it isn’t.

And when you’re under interview pressure or building fast, memory alone isn’t enough.

So I am sharing a Python Cheat Sheets PDF that brings all the essentials into one place clean, concise, and practical.

📌 What’s inside:
 ✅ Python syntax & core concepts
 ✅ Data types, loops, and functions
 ✅ OOP essentials (classes, objects, inheritance)
 ✅ File handling & exceptions
 ✅ Built-in functions you should memorize
 ✅ Useful libraries & shortcuts
 ✅ Common mistakes to avoid

📘 Download the Python Cheat Sheets.

Python-Cheatsheets

Struggling with Hallucinations, Low Recall, or Slow RAG Systems?

The problem? Most developers skip the fundamentals.

Here's what actually moves the needle:

1. Your chunking strategy is killing your accuracy
Fixed-size chunks = lost context Solution: Sliding windows with 10-15% overlap

2. Vector search alone isn't enough
Keyword searches still matter Hybrid search (semantic + BM25) catches what embeddings miss

3. Retrieval ≠ Quality
Retrieving 50 documents means nothing Reranking the top 3-5 with a cross-encoder changes everything

4. Your prompts are too vague
"Answer using the context" → Hallucinations "Answer ONLY from the context. If unsure, say I don't know" → Grounded responses
The truth about RAG systems: → Garbage in = Garbage out → 80% of performance comes from data quality → The best model won't fix bad retrieval

👇 I’ve attached the full cheat sheet PDF

Rag_Performance_Optimization

The Only DSA Cheat Sheet You’ll Ever Need.

If you’re preparing for coding interviews, this one resource can save you hundreds of hours.

A DSA Cheat Sheet that covers everything interviewers love to test in one clean, visual format:

📌 What’s inside:
 ✅ Arrays, Strings, Linked Lists, Stacks & Queues
 ✅ Trees, Tries, Graphs & Hashing
 ✅ Recursion & Backtracking
 ✅ Dynamic Programming (LCS, LIS, Knapsack, Matrix Chain)
 ✅ Searching & Sorting
 ✅ Greedy Algorithms
 ✅ Bit Manipulation & Math
 ✅ Common Coding Templates
 ✅ Beginner Mistakes to Avoid

Download / Save the DSA Cheat Sheet.

DSA_Cheatsheet

300 Real SQL Interview Questions (Medium → Advanced)

It’s not basic SELECT queries that fail people.

It’s window functions, CTEs, edge cases, and real-world logic.

So this pdf put everything into one place:

A practical, no-BS collection designed to help you actually clear technical interviews.

What’s inside:
🔹 300 real interview questions used by top companies
 🔹 Advanced JOINs, subqueries & aggregations
 🔹 Window functions (RANK, LAG, NTILE, CUME_DIST, etc.)
 🔹 Recursive CTEs & hierarchy problems
 🔹 Time-series, gaps & islands, streaks, running totals
 🔹 Real business scenarios (sales, HR, finance, ops)
 🔹 Performance-aware query patterns

🔁 Share it with someone preparing for interviews

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