*🔥 Now Let’s move to the next topic:*

*COALESCE & NULL Handling in SQL*

*🧠 1. What is NULL in SQL?*
NULL means
👉 missing value
👉 unknown value
👉 no data available

*⚠️ NULL is NOT:*
❌ 0
❌ Empty string

*⚡ 2. Problems with NULL Values*
NULL can affect:
❌ Calculations
❌ Comparisons
❌ Reports

*Example 👇*
SELECT salary + bonus
FROM employees;
If bonus is NULL → result becomes NULL ❌


*🔥 3. COALESCE Function*
👉 Replaces NULL with another value

*✅ Syntax*
COALESCE(column, value)

*⚡ 4. Basic Example*
SELECT name,
COALESCE(bonus, 0) AS bonus
FROM employees;

✔ If bonus is NULL → shows 0 instead

*⚡ 5. Multiple Values with COALESCE*
SELECT name,
COALESCE(phone, email, 'No Contact') AS contact
FROM employees;

✔ Returns first non-NULL value

*🔥 6. IS NULL & IS NOT NULL*

👉 Find employees without bonus
SELECT * FROM employees
WHERE bonus IS NULL;

👉 Find employees with bonus
SELECT * FROM employees
WHERE bonus IS NOT NULL;

*⚡ 7. NULL Handling in Aggregation*
SELECT AVG(COALESCE(bonus, 0))
FROM employees;
✔ Prevents NULL issues in calculations

*🎯 8. Practice Tasks*
1. Replace NULL bonus with 0
2. Find rows with NULL values
3. Find rows without NULL values
4. Use COALESCE with multiple columns
5. Calculate total salary safely using COALESCE

*⚡ Mini Challenge 🔥*
👉 Show employee total income:
salary + bonus
(If bonus is NULL → treat as 0)

*🔥 Mini Challenge Solution 💯*

*👉 Requirement:*
total_income = salary + bonus
If bonus is NULL → treat it as 0

*✅ SQL Solution Using COALESCE*
SELECT name,
salary,
COALESCE(bonus, 0) AS bonus,
salary + COALESCE(bonus, 0) AS total_income
FROM employees;

*✅ Example Output*
*name* : Amit, *salary* : 70000, *bonus* : 5000, *total_income* : 75000
*name* : Neha, *salary* : 50000, *bonus* : 0, *total_income* : 50000
*name* : Ravi, *salary* : 60000, *bonus* : 3000, *total_income* : 63000

*🧠 How It Works*
👉 COALESCE(bonus, 0)
means:
- If bonus exists → use bonus
- If bonus is NULL → use 0

💡 *Most common interview mistake* ❌👇

WHERE column = NULL
*✅ Correct:*
WHERE column IS NULL

*Double Tap ❤️ For More*

MIT made its entire AI & ML library 100% FREE to access.

These 12 books are the best place to start 👇

↳ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀

1. Foundations of Machine Learning
https://cs.nyu.edu/~mohri/mlbook/
The mathematical backbone of ML - algorithms, theory, and how models actually learn.

2. Understanding Deep Learning
https://udlbook.github.io/udlbook/
Neural networks explained visually and intuitively, from basics to modern architectures.

3. Deep Learning
https://www.deeplearningbook.org/
The definitive deep learning reference, written by the researchers who shaped the field.

4. Introduction to Machine Learning Systems
https://mlsysbook.ai/
How to design and build ML systems that work in production, not just in notebooks.

5. Algorithms for Optimization
https://algorithmsbook.com/optimization/
The math behind how models improve - gradient methods, search, and decision-making.

↳ 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

6. Reinforcement Learning: An Introduction
http://incompleteideas.net/book/the-book.html
T