https://www.facebook.com/940421622486093/

AI ML With Muqrrab Hussain, PO CHWOK AZAM CHAK NO 362TDA TEH AND DIST LAYYAH, Layyah (2026)

20/05/2026

𝐃𝐚𝐲 𝟏𝟗 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 🚀🐍

Today I entered the world of 𝐏𝐲𝐭𝐡𝐨𝐧 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬 and honestly… dictionaries feel like Python’s version of a super-organized notebook 📖😂

Instead of remembering positions like lists, dictionaries remember 𝐤𝐞𝐲𝐬 🔑

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬:

✅ Characteristics of Dictionaries
✅ Creation of Dictionaries
✅ Accessing items using keys
✅ Adding key-value pairs
✅ Editing key-value pairs
✅ Removing key-value pairs

✅ Dictionary Functions:
📌keys()
📌values()
📌items()
📌get()
📌update()
📌pop() and more
Slowly understanding how different data structures help organize and manage data efficiently 🧠✨

🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 🎲📚
Covered:
📌 Concept of Total Probability
📌 Binomial Probability
📌 Solving probability through Tree Diagrams 🌳

Probability is starting to feel less like guessing and more like structured logic 😄

Every day I realize AI/ML is built on small concepts connected together step by step 🤖📈
Still learning. Still improving. One concept at a time 💪

18/05/2026

𝐃𝐚𝐲 𝟏𝟖 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 🚀🐍

Today I explored 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐞𝐭𝐬 and realized they are basically Python’s way of saying:
“No duplicates allowed here 😤” 😂

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬:

✅ Characteristics & creation of Sets
✅ Mutability in Sets
❌ No indexing & slicing in Sets
❌ Editing specific items gives errors because sets are unordered

✅ Set Operations:

📌Union
📌Intersection
📌Difference
📌Symmetric Difference

✅ Special Methods:
`isdisjoint()` `issubset()` `issuperset()` `copy()`

Learning sets made me understand how useful they are for handling unique data efficiently 🧠

🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 🎲

Covered:
📌 Conditional Probability
📌 Dependent & Independent Events
📌 Solved examples to understand how probabilities change under conditions

The deeper I go into AI/ML foundations, the more I realize math + programming together create real problem-solving power 🤖📈

Small progress every day still counts 💪

16/05/2026

𝐃𝐚𝐲 𝟏𝟕 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 🚀🐍

Today was all about understanding the difference between things that can change… and things that absolutely refuse to 😅
(Yes, I’m talking about Lists vs Tuples 😂)

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

✅ List Comprehension
→ A cleaner and smarter way to create lists 🔥

✅ Started learning **Tuples**

📌Creation of tuples
📌Indexing & slicing
📌Tuple functions:
`len()`, `sum()`, `min()`, `max()`, `sorted()`, `count()`, `index()`

✅ Learned an important concept:
❌ Tuples do NOT allow editing items
(Python really said: “No changes allowed here” 😭)

✅ Difference between Lists & Tuples
✅ Why and when tuples are useful

🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 🎲📚

Today’s probability concepts felt like learning the “rules of uncertainty” 😄

Covered:
📌 Equally Likely Events
📌 Mutually Exclusive / Disjoint Events
📌 Exhaustive Events
📌 Simple & Compound Events
📌 Algebra of Events:

* Union
* Intersection
* Difference and more

📌 Addition Theorem on Probability

The deeper I go into Python and Mathematics, the more I realize that AI/ML is really built on strong fundamentals and logical thinking 🧠✨

Learning step by step. Improving day by day 📈

14/05/2026

𝐃𝐚𝐲 𝟏𝟔 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 🚀🐍

Today was basically me becoming best friends with Python Lists
The more I learn lists, the more I realize they can do almost everything except wash dishes

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

✅ Adding items in lists:

𝗮𝗽𝗽𝗲𝗻𝗱()
𝗲𝘅𝘁𝗲𝗻𝗱()
𝗶𝗻𝘀𝗲𝗿𝘁())

✅ Editing list items using indexing

✅ Deleting items from lists:

𝗱𝗲𝗹
𝗽𝗼𝗽()
𝗿𝗲𝗺𝗼𝘃𝗲()
𝗰𝗹𝗲𝗮𝗿()

✅ Operations on lists:

𝗔𝗿𝗶𝘁𝗵𝗺𝗲𝘁𝗶𝗰 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀
𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
𝗟𝗼𝗼𝗽𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗹𝗶𝘀𝘁𝘀
𝗠𝗲𝗺𝗯𝗲𝗿𝘀𝗵𝗶𝗽 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀

✅ Special Functions:

𝗹𝗲𝗻()
𝗺𝗶𝗻()
𝗺𝗮𝘅()
𝘀𝗼𝗿𝘁()
𝗰𝗼𝘂𝗻𝘁()
𝗶𝗻𝗱𝗲𝘅()
𝗿𝗲𝘃𝗲𝗿𝘀𝗲()
𝗰𝗼𝗽𝘆()

✅ Also studied the **disadvantages of lists** and where they may not be the best choice.

🔹 𝐌𝐚𝐭𝐡 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 🎲📚

Started learning Probability and honestly… it already feels like math trying to predict life 😂

Covered:
📌 Introduction & historical background of probability
📌 Classical, Axiomatic & Experimental approaches
📌 Experiment, Outcome, Event & Sample Space
📌 Classical definition of probability
📌 Fair vs Unfair situations with examples

One thing I’m realizing daily:
AI/ML isn’t just coding 🤖
It’s coding + logic + mathematics + consistency 💪

Small progress every day still counts 📈✨

13/05/2026

𝐃𝐚𝐲 𝟏𝟓 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 🚀
Today was all about entering the world of **Python Lists** 🐍 and surviving some serious math progression concepts 📚

While learning lists, I realized one thing…
Python lists are basically that one friend who can store *everything* without complaining

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

✅ What are Lists?
✅ Array vs List
✅ How lists are stored in memory
✅ Characteristics of Lists
✅ Creation of Lists
✅ Indexing & Slicing of Lists

Finally started understanding why lists are one of the most powerful data structures in Python 🔥

🔹 𝐌𝐚𝐭𝐡 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

📌 Infinite GP
📌 AGP (Arithmetico-Geometric Progression)
📌 Concepts of HP (Harmonic Progression)

Math is slowly turning from “confusing symbols” into actual patterns and logic 🧠✨

Every day feels like adding another small building block toward becoming an AI/ML Engineer 🤖

Focused on consistency, curiosity, and learning step by step 💪

08/04/2026

𝐃𝐚𝐲 𝟏𝟒 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — Exploring String Functions & Arithmetic Series
Continuing my journey toward becoming an 𝗔𝗜/𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, today was focused on learning powerful 𝘀𝘁𝗿𝗶𝗻𝗴 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 and starting a new chapter in mathematics: 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝘀𝗲𝗿𝗶𝗲𝘀.

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:
𝐒𝐭𝐫𝐢𝐧𝐠 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬:
max(), min(), len(), sorted()
𝐂𝐚𝐬𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬:
capitalize(), upper(), lower()
𝐒𝐞𝐚𝐫𝐜𝐡 & 𝐂𝐨𝐮𝐧𝐭:
count(), find(), index()
𝐒𝐭𝐫𝐢𝐧𝐠 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬:
split(), join()
🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

Introduction to 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐬 𝐚𝐧𝐝 𝐒𝐞𝐫𝐢𝐞𝐬
Explored the 𝐡𝐢𝐬𝐭𝐨𝐫𝐲 𝐚𝐧𝐝 𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐨𝐧 behind how these concepts were developed
Studied 𝐀𝐫𝐢𝐭𝐡𝐦𝐞𝐭𝐢𝐜 𝐒𝐞𝐫𝐢𝐞𝐬
Understanding string manipulation helps in 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐭𝐞𝐱𝐭 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠, while sequences and series strengthen 𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠, both essential for machine learning.

Focused on 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲, 𝐜𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩.

04/03/2026

𝐃𝐚𝐲 𝟏𝟑 | 𝐀𝐈/𝐌𝐋 𝐉𝐮𝐫𝐧𝐞𝐲 — Working with String Operations & Combinatorics

Continuing my journey toward becoming an AI/ML Engineer, today was focused on understanding how operators work with strings in Python and strengthening foundational concepts in permutation and combination.

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

Studied operators applied to strings, including:

Arithmetic Operators → Concatenation and repetition of strings

Relational Operators → Comparing strings lexicographically

Membership Operators → Checking if a substring exists (in, not in)

Boolean Operators → Logical evaluation in expressions

Practiced using loops with strings to iterate through characters

🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

Concept of Factorial

Permutation and Combination Concepts

Understanding how strings behave in programming and learning counting techniques in mathematics strengthens the analytical thinking and programming foundations required for machine learning.

Focused on consistency, practice, and learning step by step.

03/03/2026

𝐃𝐚𝐲 𝟏𝟐 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — Back to Learning & Exploring Strings

After a short break working on a coding project, I resumed my journey toward becoming an AI/ML Engineer.
Today was focused on understanding string handling in Python and continuing my progress in combinatorics.

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

Creating and working with strings

Indexing of strings → Positive and negative indexing

String slicing → Accessing parts of text efficiently

🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

Combinations → Understanding selection of items where order does not matter

Learning how to manipulate text in Python and studying combinatorial reasoning strengthens the analytical thinking and programming foundation required for machine learning.

21/01/2026

𝐃𝐚𝐲 𝟏𝟏 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — Practicing Patterns & Strengthening Counting Principles

Continuing my journey toward becoming an 𝐀𝐈/𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫, today was focused on 𝐝𝐞𝐞𝐩 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐰𝐢𝐭𝐡 𝐟𝐨𝐫 𝐥𝐨𝐨𝐩𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 and strengthening a key 𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐢𝐧 𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬.

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

Solved multiple 𝙛𝙤𝙧 𝙡𝙤𝙤𝙥 𝙥𝙧𝙤𝙗𝙡𝙚𝙢𝙨
Built various 𝙥𝙖𝙩𝙩𝙚𝙧𝙣𝙨 𝙪𝙨𝙞𝙣𝙜 𝙛𝙤𝙧 𝙡𝙤𝙤𝙥𝙨, including:
Number patterns
Star 【*】 patterns
Progressed from 𝐛𝐚𝐬𝐢𝐜 𝐭𝐨 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐥𝐨𝐠𝐢𝐜
Improved control over nested loops and iteration flow

🔹 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:

𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥 𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 → Understanding how choices add up in counting problems
Practicing loop-based pattern problems and learning counting principles helps build 𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠, 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, 𝐚𝐧𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐛𝐥𝐞𝐦-𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬, which are essential for machine learning.

20/01/2026

𝐃𝐚𝐲 𝟏𝟎 | 𝐀𝐈/𝐌𝐋 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — Strengthening Iteration & Combinatorial Thinking

Continuing my journey toward becoming an AI/ML Engineer, today was focused on mastering iteration in Python and starting a new chapter in mathematics that builds logical and analytical thinking.

🔹 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:
Studied the 𝐟𝐨𝐫 𝐥𝐨𝐨𝐩 in detail
Practiced multiple 𝐟𝐨𝐫 𝐥𝐨𝐨𝐩 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬
Improved understanding of iteration, ranges, and loop-based logic

🔹𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝:
Started a new chapter: 𝐏𝐞𝐫𝐦𝐮𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧
𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞→ Understanding how choices combine

Learning iteration in programming along with combinatorial reasoning in mathematics helps develop the 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐛𝐥𝐞𝐦-𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐦𝐢𝐧𝐝𝐬𝐞𝐭 required for machine learning.

AI ML With Muqrrab Hussain

20/05/2026

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