Sunday, September 14, 2025

IELTS Grammar Basic to Advanced (Part1 and Part2)

 


📘 Part 1: Parts of Speech (āĻļāĻŦ্āĻĻেāϰ āĻĒ্āϰāĻ•াāϰāĻ­েāĻĻ)

āχংāϰেāϜি āĻ­াāώা⧟ āĻŽোāϟ ā§Ž āϧāϰāύেāϰ āĻļāĻŦ্āĻĻ āφāĻ›ে āϝেāĻ—ুāϞোāĻ•ে Parts of Speech āĻŦāϞা āĻšā§Ÿ। āĻāĻ—ুāϞো āĻŦাāĻ•্āϝ āĻ—āĻ āύেāϰ āĻ­িāϤ্āϤি। āϚāϞুāύ āĻāĻ• āĻāĻ• āĻ•āϰে āϏāĻšāϜ āĻŦাংāϞা⧟ āĻŦ্āϝাāĻ–্āϝা āĻ•āϰি।


đŸŸĻ 1. Noun (āĻŦিāĻļেāώ্āϝ)

Noun āĻāĻŽāύ āĻāĻ•āϟি āĻļāĻŦ্āĻĻ āϝা āĻŽাāύুāώ, āĻĒ্āϰাāĻŖী, āĻŦāϏ্āϤু, āϏ্āĻĨাāύ āĻŦা āϧাāϰāĻŖা āĻŦোāĻা⧟।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

Person (āĻŦ্āϝāĻ•্āϤি): teacher, Rahim, girl

Place (āϏ্āĻĨাāύ): Dhaka, school, park

Thing (āĻŦāϏ্āϤু): pen, table, phone

Idea (āϧাāϰāĻŖা): freedom, love, honesty

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖ:

The teacher is very kind.

Love is powerful.

đŸŸĻ 2. Pronoun (āϏāϰ্āĻŦāύাāĻŽ)

Pronoun āĻšāϞো āĻāĻŽāύ āĻļāĻŦ্āĻĻ āϝা Noun-āĻāϰ āĻĒāϰিāĻŦāϰ্āϤে āĻŦāϏে āϝাāϤে āĻŦাāϰāĻŦাāϰ Noun āύা āϞিāĻ–āϤে āĻšā§Ÿ।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

I, you, he, she, it, we, they, him, her, us, them

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖ:

Rahim is a student. He is very good.

Rina and I are friends. We play together.

đŸŸĻ 3. Verb (āĻ•্āϰি⧟া)

Verb āĻšāϞো āĻāĻŽāύ āĻļāĻŦ্āĻĻ āϝা āĻ•োāύো āĻ•াāϜ, āĻ…āĻŦāϏ্āĻĨা āĻŦা āĻ…āϏ্āϤিāϤ্āĻŦ āĻŦোāĻা⧟।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

run, go, eat, play, read, be, have, do

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖ:

I go to school.

She is happy.

They have a car.

đŸŸĻ 4. Adjective (āĻŦিāĻļেāώāĻŖ)

Adjective āĻāĻŽāύ āĻļāĻŦ্āĻĻ āϝা noun āĻŦা pronoun-āĻāϰ āĻ—ুāĻŖ āĻŦা āĻŦৈāĻļিāώ্āϟ্āϝ āĻĒ্āϰāĻ•াāĻļ āĻ•āϰে।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

good, big, small, red, intelligent

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖ:

He is a good boy.

I bought a red pen.

đŸŸĻ 5. Adverb (āĻ•্āϰি⧟া āĻŦিāĻļেāώāĻŖ)

Adverb āĻāĻŽāύ āĻļāĻŦ্āĻĻ āϝা verb, adjective āĻŦা āĻ…āύ্āϝ adverb-āĻ•ে āĻŦিāĻļেāώāĻŖ āĻ•āϰে।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

quickly, very, slowly, always, well

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖ:

He runs quickly.

She is very intelligent.

đŸŸĻ 6. Preposition (āĻĒāĻĻাāĻŦāϞী)

Preposition āĻāĻŽāύ āĻļāĻŦ্āĻĻ āϝা noun āĻŦা pronoun-āĻāϰ āϏাāĻĨে āĻŦাāĻ•্āϝেāϰ āĻ…āύ্āϝ āĻ…ংāĻļেāϰ āϏāĻŽ্āĻĒāϰ্āĻ• āĻŦোāĻা⧟। āĻŦেāĻļিāϰāĻ­াāĻ— āϏāĻŽā§Ÿ āĻ…āĻŦāϏ্āĻĨাāύ āĻŦা āϏāĻŽā§Ÿ āĻŦোāĻাāϤে āĻŦ্āϝāĻŦāĻšৃāϤ āĻšā§Ÿ।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

in, on, at, under, behind, by, with

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖ:

The book is on the table.

I live in Dhaka.

đŸŸĻ 7. Conjunction (āϏংāϝোāĻ—č¯)

Conjunction āĻšāϞো āϏংāϝোāĻ—āĻ•াāϰী āĻļāĻŦ্āĻĻ, āϝা āĻĻুāϟি āĻļāĻŦ্āĻĻ, āĻŦাāĻ•্āϝাংāĻļ āĻŦা āĻŦাāĻ•্āϝāĻ•ে āϝুāĻ•্āϤ āĻ•āϰে।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

and, but, or, so, because, although

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖঃ

I like tea and coffee.

He is poor but honest.

đŸŸĻ 8. Interjection (āωāĻĻ্āĻŦোāϧāĻ¨č¯)

Interjection āĻšāϞো āφāĻŦেāĻ— āĻĒ্āϰāĻ•াāĻļāĻ•াāϰী āĻļāĻŦ্āĻĻ। āĻšāĻ াā§Ž āĻ•োāύো āĻ…āύুāĻ­ূāϤি āĻŦোāĻাāϤে āĻŦ্āϝāĻŦāĻšৃāϤ āĻšā§Ÿ।

🔹 āωāĻĻাāĻšāϰāĻŖঃ

Wow! Oh! Oops! Alas! Hurray!

🔹 āĻŦাāĻ•্āϝে āĻŦ্āϝāĻŦāĻšাāϰে āωāĻĻাāĻšāϰāĻŖঃ

Wow! What a beautiful dress!

Alas! He is dead.


📘 Part 2: Tense (āĻ•াāϞ) – IELTS-āĻāϰ āϜāύ্āϝ āĻ—ুāϰুāϤ্āĻŦāĻĒূāϰ্āĻŖ

Tense āĻŦোāĻা⧟ āĻ•োāύো āĻ•াāϜ āĻ•āĻ–āύ āϘāϟāĻ›ে – āĻ…āϤীāϤে, āĻŦāϰ্āϤāĻŽাāύে āύা āĻ­āĻŦিāώ্āϝāϤে। āϤিāύāϟি āĻĒ্āϰāϧাāύ Tense āφāĻ›ে:

đŸ”ĩ ā§§. Present Tense (āĻŦāϰ্āϤāĻŽাāύ āĻ•াāϞ)

āϝāĻ–āύ āĻ•াāϜāϟি āĻāĻ–āύ āϚāϞāĻ›ে āĻŦা āϏাāϧাāϰāĻŖāĻ­াāĻŦে āϘāϟে āĻĨাāĻ•ে।

🔹 a) Present Simple Tense

āĻ—āĻ āύঃ Subject + base verb (+ s/es)

 āωāĻĻাāĻšāϰāĻŖঃ

I play football.

He plays football.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āϏাāϧাāϰāĻŖ āϏāϤ্āϝ āĻŦা āĻ…āĻ­্āϝাāϏ

The sun rises in the east.

🔹 b) Present Continuous Tense

āĻ—āĻ āύঃ Subject + am/is/are + verb + ing

 āωāĻĻাāĻšāϰāĻŖঃ

I am reading a book.

She is cooking now.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻāĻ–āύāχ āϚāϞāĻ›ে āĻāĻŽāύ āĻ•াāϜ

He is watching TV.

🔹 c) Present Perfect Tense

āĻ—āĻ āύঃ Subject + have/has + past participle (V3)

 āωāĻĻাāĻšāϰāĻŖঃ

I have finished my homework.

She has gone to school.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ…āϤীāϤে āĻļুāϰু āĻšā§Ÿে āĻāĻ–āύো āĻĒ্āϰāĻ­াāĻŦ āφāĻ›ে

I have lived here for 5 years.

🔹 d) Present Perfect Continuous

āĻ—āĻ āύঃ Subject + have/has been + verb + ing

 āωāĻĻাāĻšāϰāĻŖঃ

I have been studying since morning.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ…āϤীāϤে āĻļুāϰু āĻšā§ŸেāĻ›ে, āĻāĻ–āύো āϚāϞāĻ›ে

They have been playing for 2 hours.

đŸ”ĩ ⧍. Past Tense (āĻ…āϤীāϤ āĻ•াāϞ)

āϝāĻ–āύ āĻ•াāϜāϟি āĻ…āϤীāϤে āϘāϟেāĻ›ে।

🔹 a) Past Simple

āĻ—āĻ āύঃ Subject + verb (past form)

 āωāĻĻাāĻšāϰāĻŖঃ

I went to school yesterday.

She ate rice.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻāĻ•েāĻŦাāϰে āĻļেāώ āĻšā§Ÿে āĻ—েāĻ›ে āĻāĻŽāύ āĻ•াāϜ

I saw a movie last night.

🔹 b) Past Continuous

āĻ—āĻ āύঃ Subject + was/were + verb + ing

 āωāĻĻাāĻšāϰāĻŖঃ

I was reading a book.

They were playing cricket.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āϤāĻ–āύ āĻ•োāύো āϏāĻŽā§Ÿ āϚāϞāĻ›িāϞ āĻāĻŽāύ āĻ•াāϜ

It was raining in the morning.

🔹 c) Past Perfect

āĻ—āĻ āύঃ Subject + had + past participle (V3)

 āωāĻĻাāĻšāϰāĻŖঃ

I had eaten before you came.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ…āϤীāϤে āĻ•োāύো āĻ•াāϜ āĻ…āύ্āϝ āĻ•াāϜেāϰ āφāĻ—ে āĻļেāώ āĻšā§ŸেāĻ›ে

She had left before I arrived.

🔹 d) Past Perfect Continuous

āĻ—āĻ āύঃ Subject + had been + verb + ing

 āωāĻĻাāĻšāϰāĻŖঃ

He had been working for 5 hours.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ…āϤীāϤে āĻĻীāϰ্āϘ āϏāĻŽā§Ÿ āϧāϰে āϚāϞāĻ›িāϞ

They had been studying before the exam.

đŸ”ĩ ā§Š. Future Tense (āĻ­āĻŦিāώ্āĻ¯ā§Ž āĻ•াāϞ)

āϝāĻ–āύ āĻ•াāϜāϟি āĻ­āĻŦিāώ্āϝāϤে āĻšāĻŦে।

🔹 a) Future Simple

āĻ—āĻ āύঃ Subject + will + base verb

 āωāĻĻাāĻšāϰāĻŖঃ

I will go to school tomorrow.

She will come later.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ­āĻŦিāώ্āϝāϤেāϰ āϏাāϧাāϰāĻŖ āĻ•াāϜ

We will visit Cox's Bazar.

🔹 b) Future Continuous

āĻ—āĻ āύঃ Subject + will be + verb + ing

 āωāĻĻাāĻšāϰāĻŖঃ

I will be studying at 10 PM.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ­āĻŦিāώ্āϝāϤে āύিāϰ্āĻĻিāώ্āϟ āϏāĻŽā§Ÿে āϚāϞāĻŦে

He will be driving at that time.

🔹 c) Future Perfect

āĻ—āĻ āύঃ Subject + will have + past participle

 āωāĻĻাāĻšāϰāĻŖঃ

I will have finished the work by 5 PM.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āύিāϰ্āĻĻিāώ্āϟ āϏāĻŽā§Ÿেāϰ āĻŽāϧ্āϝে āĻ­āĻŦিāώ্āϝāϤে āĻ•াāϜ āĻļেāώ āĻšāĻŦে

They will have arrived by noon.

🔹 d) Future Perfect Continuous

āĻ—āĻ āύঃ Subject + will have been + verb + ing

 āωāĻĻাāĻšāϰāĻŖঃ

I will have been working for 3 hours by 6 PM.

✅ āĻŦ্āϝāĻŦāĻšাāϰ:

āĻ­āĻŦিāώ্āϝāϤেāϰ āĻāĻ•āϟি āϏāĻŽā§Ÿে āĻĻীāϰ্āϘ āϏāĻŽā§Ÿ āϧāϰে āϚāϞāĻŦে

He will have been waiting for 2 hours.

✅ IELTS-āĻ Tense-āĻāϰ āĻŦ্āϝāĻŦāĻšাāϰ:

Writing Task 1 (Academic): āĻŦেāĻļি Passive Voice + Past/Present Perfect

Writing Task 2: Present + Modal Verbs

Speaking: Present Simple, Present Perfect, Past Simple, Future Simple

Listening/Reading: āϏāĻ•āϞ Tense āφāϏāϤে āĻĒাāϰে

📌 āϟিāĻĒāϏ:

"I go" ≠ "I am going" ≠ "I have gone" – āĻ…āϰ্āĻĨ āĻ“ āĻŦ্āϝāĻŦāĻšাāϰ āϏāĻŽ্āĻĒূāϰ্āĻŖ āφāϞাāĻĻা।

Tense āĻ িāĻ• āύা āĻšāϞে sentence-āĻāϰ āĻŽাāύে āĻ­ুāϞ āĻšāϤে āĻĒাāϰে।


āϝāĻĻি āϏāĻŦ āĻĒাāϰ্āϟ āĻāϰ āĻĒ্āϰ⧟োāϜāύ āĻšā§Ÿ āϤাāĻšāϞে āĻ•āĻŽেāύ্āϟ āĻ•āϰুāύ। 

Saturday, July 26, 2025

āĻŽেāĻļিāύ āϞাāϰ্āύিং āĻ•ী?

 

āĻŽেāĻļিāύ āϞাāϰ্āύিং āĻ•ী?

āĻŽেāĻļিāύ āϞাāϰ্āύিং  āĻšāϞো āĻāĻ•āϟি āĻĒ্āϰāϝুāĻ•্āϤিāĻ­িāϤ্āϤিāĻ• āĻļাāĻ–া, āϝেāĻ–াāύে āĻĒ্āϰোāĻ—্āϰাāĻŽিং, āĻ…্āϝাāϞāĻ—āϰিāĻĻāĻŽ, āĻ—āĻŖিāϤ, āĻĒāϰিāϏংāĻ–্āϝাāύ āĻāĻŦং āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ āĻāĻ•āϤ্āϰে āĻ•াāϜ āĻ•āϰে āĻāĻŽāύ āĻāĻ•āϟি āϏিāϏ্āϟেāĻŽ āϤৈāϰি āĻ•āϰāϤে āϝাāϰ āĻŽাāϧ্āϝāĻŽে āĻ•āĻŽ্āĻĒিāωāϟাāϰ āĻĄেāϟা āĻĨেāĻ•ে āĻļেāĻ–ে āĻāĻŦং āĻ­āĻŦিāώ্āĻ¯ā§Ž āĻ…āύুāĻŽাāύ āĻ•āϰāϤে āĻĒাāϰে।


ā§§. āϝে āĻŦিāώāϝ়āĻ—ুāϞো āϜাāύা āϜāϰুāϰি

✅ āĻĒ্āϰোāĻ—্āϰাāĻŽিং āϏ্āĻ•িāϞ

  • Python (āϏāϰ্āĻŦাāϧিāĻ• āĻŦ্āϝāĻŦāĻšৃāϤ āĻ­াāώা)

  • R, Java, C++, Scala (āĻĒ্āϰāϜেāĻ•্āϟ āĻ…āύুāϝা⧟ী)

  • āϜুāĻĒিāϟাāϰ āύোāϟāĻŦুāĻ•, āĻ•োāĻĄ āĻāĻĄিāϟāϰ (VS Code, PyCharm)

✅ āĻ—āĻŖিāϤ āĻ“ āĻĒāϰিāϏংāĻ–্āϝাāύ

  • āϞিāύিāϝ়াāϰ āĻ…্āϝাāϞāϜেāĻŦ্āϰা (Matrix, Vector)

  • Probability & Statistics (Distribution, Mean, Variance)

  • Calculus (Gradient Descent, Cost Function)

  • Optimization (Min/Max Functions)

✅ āĻĄেāϟা āϏ্āϟ্āϰাāĻ•āϚাāϰ āĻ“ āĻ…্āϝাāϞāĻ—āϰিāĻĻāĻŽ

  • Array, Stack, Queue, Linked List, Tree, Graph

  • Searching & Sorting

  • Dynamic Programming


⧍. āĻŽেāĻļিāύ āϞাāϰ্āύিং āĻāϰ āĻĒ্āϰāĻ•াāϰāĻ­েāĻĻ

✅ ā§§. Supervised Learning (āύিāϰ্āĻĻেāĻļিāϤ āĻļেāĻ–া)

āωāĻĻাāĻšāϰāĻŖ: House Price Prediction, Email Spam Detection

✅ ⧍. Unsupervised Learning (āĻ…-āύিāϰ্āĻĻেāĻļিāϤ āĻļেāĻ–া)

āωāĻĻাāĻšāϰāĻŖ: Customer Segmentation, Anomaly Detection

✅ ā§Š. Reinforcement Learning (āĻĒুāϰāϏ্āĻ•াāϰ-āĻ­িāϤ্āϤিāĻ• āĻļেāĻ–া)

āωāĻĻাāĻšāϰāĻŖ: Game AI, Robotics, Self-driving cars


ā§Š. āĻ—ুāϰুāϤ্āĻŦāĻĒূāϰ্āĻŖ āϞাāχāĻŦ্āϰেāϰি āĻ“ āϟুāϞāϏ

🔹 Python āϞাāχāĻŦ্āϰেāϰি

  • NumPy – āĻ—াāĻŖিāϤিāĻ• āĻ…āĻĒাāϰেāĻļāύ

  • Pandas – āĻĄেāϟা āĻĒ্āϰāϏেāϏিং āĻ“ āĻŦিāĻļ্āϞেāώāĻŖ

  • Matplotlib/Seaborn – Visualization

  • Scikit-learn – Classical ML Models

  • TensorFlow / PyTorch – Deep Learning

🔹 āϟুāϞāϏ āĻ“ āĻĢ্āϰেāĻŽāĻ“ā§Ÿাāϰ্āĻ•

  • Jupyter Notebook

  • Google Colab

  • Git & GitHub

  • Docker (Deployment āĻāϰ āϜāύ্āϝ)

  • MLflow (Model Tracking)

  • Airflow (Pipeline Scheduling)


 ā§Ē. āĻŽāĻĄেāϞ āϤৈāϰিāϰ āϧাāĻĒ

  1. Problem Understanding

  2. Data Collection

  3. Data Cleaning & Preprocessing

  4. Feature Engineering

  5. Model Selection & Training

  6. Model Evaluation (Accuracy, F1 Score, Confusion Matrix)

  7. Hyperparameter Tuning

  8. Deployment (API / Web App)

  9. Monitoring & Retraining


ā§Ģ. āĻĄিāĻĒ āϞাāϰ্āύিং āĻ“ āύিāωāϰাāϞ āύেāϟāĻ“ā§Ÿাāϰ্āĻ•

🔹 Deep Learning āĻŦিāώāϝ়āĻ­িāϤ্āϤিāĻ• āϟāĻĒিāĻ•:

  • ANN (Artificial Neural Networks)

  • CNN (Image Processing)

  • RNN/LSTM (Sequence Data - āϝেāĻŽāύ āĻ­াāώা)

  • Transformer (NLP, āϝেāĻŽāύ ChatGPT)


 ā§Ŧ. Production Deployment

  • Flask / FastAPI āĻĻি⧟ে API āĻŦাāύাāύো

  • Docker āĻĻি⧟ে āĻ•āύ্āϟেāχāύাāϰাāχāϜ āĻ•āϰা

  • AWS / Google Cloud / Heroku āϤে āĻĄেāĻĒ্āϞāϝ়āĻŽেāύ্āϟ

  • Continuous Integration & Deployment (CI/CD)


ā§­. āĻļেāĻ–াāϰ āϰোāĻĄāĻŽ্āϝাāĻĒ (Roadmap)

āϧাāĻĒে āϧাāĻĒে:

  1. Python āĻĒ্āϰোāĻ—্āϰাāĻŽিং āĻļেāĻ–া

  2. āĻ—āĻŖিāϤ āĻ“ āϏ্āϟ্āϝাāϟিāϏ্āϟিāĻ•āϏ

  3. āĻĄেāϟা āϏ্āϟ্āϰাāĻ•āϚাāϰ āĻ“ āĻ…্āϝাāϞāĻ—āϰিāĻĻāĻŽ

  4. ML āϞাāχāĻŦ্āϰেāϰি (sklearn, pandas)

  5. āĻŽāĻĄেāϞ āϤৈāϰিāϰ āĻĒ্āϰ্āϝাāĻ•āϟিāϏ

  6. Deep Learning (PyTorch/TensorFlow)

  7. āϰি⧟েāϞ-āĻ“ā§Ÿাāϰ্āϞ্āĻĄ āĻĒ্āϰāϜেāĻ•্āϟ āĻ“ API āĻĄেāĻĒ্āϞ⧟āĻŽেāύ্āϟ

  8. Kaggle āĻŦা āĻ—িāϟāĻšাāĻŦে āĻĒ্āϰāϜেāĻ•্āϟ āφāĻĒāϞোāĻĄ


ā§Ž. āĻ•্āϝাāϰিāϝ়াāϰ āĻ“ āϚাāĻ•āϰিāϰ āϏুāϝোāĻ—

Job Roles:

  • Machine Learning Engineer

  • Data Scientist

  • AI Researcher

  • MLOps Engineer

  • NLP Engineer

 āĻ—ā§œ āĻŦেāϤāύ (Salary):

  • āϝুāĻ•্āϤāϰাāϜ্āϝ: £40,000 – £100,000+

  • āĻŦাংāϞাāĻĻেāĻļ: ৳50,000 – ৳200,000+

  • āϝুāĻ•্āϤāϰাāώ্āϟ্āϰ: $100,000 – $180,000+


⧝. āĻ•োāĻĨা āĻĨেāĻ•ে āĻļেāĻ–া āϝাāĻŦে?

  • Coursera, edX, Udemy, Kaggle

  • Google AI Crash Course

  • YouTube (āĻŦাংāϞা āĻ­াāώাāϝ়): Code With Wasi, Learn With Sumit (ML Playlist)

 ā§§ā§Ļ. āĻļেāĻ–াāϰ āϜāύ্āϝ āĻĒ্āϰāϜেāĻ•্āϟ āφāχāĻĄিāϝ়া

  1. Movie Recommendation System

  2. Handwritten Digit Recognizer

  3. Stock Price Prediction

  4. Face Recognition System

  5. Chatbot using NLP


 āωāĻĒāϏংāĻšাāϰ

Machine Learning Engineering āĻāĻ•āϟি āϚ্āϝাāϞেāĻž্āϜিং āĻ•িāύ্āϤু āĻĻাāϰুāĻŖ āϞাāĻ­āϜāύāĻ• āĻ“ āĻŽāϜাāĻĻাāϰ āĻ•্āώেāϤ্āϰ। āφāĻĒāύি āϝāĻĻি āϧৈāϰ্āϝ āϏāĻšāĻ•াāϰে āĻ•োāĻĄ āĻļেāĻ–েāύ, āĻ—āĻŖিāϤ āĻŦুāĻেāύ āĻ“ āĻŦাāϏ্āϤāĻŦ āϏāĻŽāϏ্āϝাāϰ āϏāĻŽাāϧাāύে āφāĻ—্āϰāĻšী āĻšāύ — āϤাāĻšāϞে āĻāχ āĻ•্āώেāϤ্āϰ āφāĻĒāύাāϰ āϜāύ্āϝ āφāĻĻāϰ্āĻļ।

Tuesday, July 1, 2025

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং: āφāϧুāύিāĻ• āĻĒ্āϰāϝুāĻ•্āϤি āĻĻুāύিāϝ়াāϰ āĻŽেāϰুāĻĻāĻŖ্āĻĄ

 

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং: āφāϧুāύিāĻ• āĻĒ্āϰāϝুāĻ•্āϤি āĻĻুāύিāϝ়াāϰ āĻŽেāϰুāĻĻāĻŖ্āĻĄ




āĻŦāϰ্āϤāĻŽাāύ āĻŦিāĻļ্āĻŦে āĻĒ্āϰāϝুāĻ•্āϤিāϰ āωāύ্āύāϝ়āύেāϰ āĻŽূāϞ āϚাāϞিāĻ•া āĻļāĻ•্āϤি āĻšāϞো āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ। āĻŽোāĻŦাāχāϞ āĻ…্āϝাāĻĒ, āĻ“āϝ়েāĻŦāϏাāχāϟ, āĻ—েāĻŽ, āϚিāĻ•িā§ŽāϏা āϝāύ্āϤ্āϰ, āĻŦ্āϝাংāĻ•িং āϏিāϏ্āϟেāĻŽ – āϏāĻŦāĻ•িāĻ›ুāϤেāχ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰেāϰ āĻŦ্āϝāĻŦāĻšাāϰ āϰāϝ়েāĻ›ে। āĻāχ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰāĻ—ুāϞো āύিāϰ্āĻŽাāĻŖ, āϰāĻ•্āώāĻŖাāĻŦেāĻ•্āώāĻŖ āĻāĻŦং āωāύ্āύāϤ āĻ•āϰাāϰ āĻ•াāϜāϟাāχ āĻ•āϰে āĻĨাāĻ•েāύ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰāϰা। āφāϰ āĻāχ āĻĒুāϰো āĻĒ্āϰāĻ•্āϰিāϝ়াāĻ•েāχ āĻŦāϞা āĻšāϝ় āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং (Software Engineering)

āĻāχ āĻŦ্āϞāĻ—ে āφāĻŽāϰা āϜাāύāĻŦো āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻ•ী, āĻāϟি āĻ•েāύ āĻ—ুāϰুāϤ্āĻŦāĻĒূāϰ্āĻŖ, āĻ•ীāĻ­াāĻŦে āĻāχ āĻĒেāĻļাāϝ় āφāϏা āϝাāϝ়, āĻāϰ āϚ্āϝাāϞেāĻž্āϜ āĻ“ āϏুāϝোāĻ—-āϏুāĻŦিāϧা, āĻāĻŦং āĻŦাংāϞাāĻĻেāĻļেāϰ āĻĒ্āϰেāĻ•্āώাāĻĒāϟে āĻāϰ āĻ­āĻŦিāώ্āĻ¯ā§Ž।


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻ•ী?

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻšāϞো āĻāĻŽāύ āĻāĻ•āϟি āĻĒ্āϰāĻ•ৌāĻļāϞ āĻļাāĻ–া āϝা āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āϏিāϏ্āϟেāĻŽ āϤৈāϰি, āωāύ্āύāϝ়āύ, āϟেāϏ্āϟিং āĻ“ āϰāĻ•্āώāĻŖাāĻŦেāĻ•্āώāĻŖেāϰ āϏাāĻĨে āϜāĻĄ়িāϤ। āĻāϟি āĻļুāϧু āĻ•োāĻĄ āϞেāĻ–াāϰ āĻ•াāϜ āύāϝ়; āĻŦāϰং āĻāϤে āĻĨাāĻ•ে āĻĒāϰিāĻ•āϞ্āĻĒāύা, āύāĻ•āĻļা (design), āĻĒ্āϰāϝ়োāĻ— (implementation), āĻĒāϰীāĻ•্āώāĻŖ (testing), āĻāĻŦং āϰāĻ•্āώāĻŖাāĻŦেāĻ•্āώāĻŖ (maintenance) – āϏāĻŦāĻ•িāĻ›ুāχ।

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰāϰা āĻŦিāĻ­িāύ্āύ āϏāĻŽāϏ্āϝাāϰ āĻ•াāϰ্āϝāĻ•āϰ āϏāĻŽাāϧাāύ āϤৈāϰি āĻ•āϰেāύ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰেāϰ āĻŽাāϧ্āϝāĻŽে, āϝাāϤে āĻŦ্āϝāĻŦāĻšাāϰāĻ•াāϰীāĻĻেāϰ āϜāύ্āϝ āύিāϰ্āĻ­āϰāϝোāĻ—্āϝ āĻ“ āϏāĻšāϜāϞāĻ­্āϝ āĻĒ্āϰāϝুāĻ•্āϤি āϤৈāϰি āĻšāϝ়।


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻ•েāύ āĻ—ুāϰুāϤ্āĻŦāĻĒূāϰ্āĻŖ?

ā§§. āϜীāĻŦāύেāϰ āϏāĻŦ āĻ•্āώেāϤ্āϰে āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ:
āφāϜāĻ•েāϰ āĻĻিāύে āĻļিāĻ•্āώা, āϏ্āĻŦাāϏ্āĻĨ্āϝ, āĻŦ্āϝāĻŦāϏা, āĻŦিāύোāĻĻāύ – āϏāĻŦ āĻ•িāĻ›ুāϤেāχ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āĻĻāϰāĻ•াāϰ। āĻāϰ āĻŽাāύে āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰāĻĻেāϰ āϚাāĻšিāĻĻা āϏāĻŦāĻ–াāύেāχ।

⧍. āĻŦিāĻļ্āĻŦেāϰ āĻĻ্āϰুāϤāϤāĻŽ āωāĻĻীāϝ়āĻŽাāύ āĻ–াāϤ:
āĻ—্āϞোāĻŦাāϞ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāύ্āĻĄাāϏ্āϟ্āϰি āĻĒ্āϰāϤি āĻŦāĻ›āϰ āĻŦিāϞিāϝ়āύ āĻĄāϞাāϰ āĻ†ā§Ÿ āĻ•āϰে। āϚাāĻšিāĻĻা āĻ…āύুāϝাāϝ়ী āĻāχ āĻ–াāϤে āĻĻāĻ•্āώ āϜāύāĻļāĻ•্āϤিāϰ āĻ…āĻ­াāĻŦ āϰāϝ়ে āĻ—েāĻ›ে।

ā§Š. āωāύ্āύāϝ়āύ āĻ“ āϏ্āĻŦāύিāϰ্āĻ­āϰāϤা:
āĻāĻ•āϟি āĻĻেāĻļ āĻĒ্āϰāϝুāĻ•্āϤিāϤে āϝāϤ āĻŦেāĻļি āĻĻāĻ•্āώ, āϤāϤāχ āωāύ্āύāϤ āĻ“ āϏ্āĻŦāύিāϰ্āĻ­āϰ āĻšāϤে āĻĒাāϰে।


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰāĻĻেāϰ āĻĻাāϝ়িāϤ্āĻŦ āĻ“ āĻ•াāϜ

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰāϰা āĻŦিāĻ­িāύ্āύ āϧāϰāύেāϰ āĻ•াāϜ āĻ•āϰেāύ, āϝাāϰ āĻŽāϧ্āϝে āωāϞ্āϞেāĻ–āϝোāĻ—্āϝ:

  • āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āĻĄিāϜাāχāύ āĻ•āϰা

  • āĻ•োāĻĄ āϞেāĻ–া āĻ“ āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ

  • āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āϟেāϏ্āϟিং (āĻŦাāĻ— āϚিāĻš্āύিāϤ āĻ•āϰা āĻ“ āϏāĻŽাāϧাāύ)

  • āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āĻŽেāχāύāϟেāύ্āϝাāύ্āϏ

  • āĻĄāĻ•ুāĻŽেāύ্āϟেāĻļāύ āϤৈāϰি

  • āĻ•্āϞাāϝ়েāύ্āϟেāϰ āϏাāĻĨে āϝোāĻ—াāϝোāĻ— āĻ“ āϚাāĻšিāĻĻা āĻŦোāĻা

āĻ…āύেāĻ• āϏāĻŽāϝ় āϤাāϰা āĻĻāϞāĻ—āϤāĻ­াāĻŦে (Team-based) āĻ•াāϜ āĻ•āϰেāύ — āϝেāĻŽāύঃ āĻĢ্āϰāύ্āϟ-āĻāύ্āĻĄ, āĻŦ্āϝাāĻ•-āĻāύ্āĻĄ, āχāωāϜাāϰ āχāύ্āϟাāϰāĻĢেāϏ āĻĄিāϜাāχāύ āχāϤ্āϝাāĻĻি।


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰ āĻšāĻ“āϝ়াāϰ āĻĒāĻĨ

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰ āĻšāϤে āĻšāϞে āφāĻĒāύাāĻ•ে āύিāϰ্āĻĻিāώ্āϟ āĻ•িāĻ›ু āϧাāĻĒ āĻ…āύুāϏāϰāĻŖ āĻ•āϰāϤে āĻšāĻŦে:

ā§§. āĻļিāĻ•্āώাāĻ—āϤ āϝোāĻ—্āϝāϤা:

  • āϏাāϧাāϰāĻŖāϤ āĻ•āĻŽ্āĻĒিāωāϟাāϰ āϏাāϝ়েāύ্āϏ, āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং, āĻ…āĻĨāĻŦা āχāύāĻĢāϰāĻŽেāĻļāύ āϟেāĻ•āύোāϞāϜি āĻŦিāώāϝ়ে āϏ্āύাāϤāĻ• (BSc) āĻĄিāĻ—্āϰি āĻĒ্āϰ⧟োāϜāύ āĻšā§Ÿ।

  • āϤāĻŦে āĻŦāϰ্āϤāĻŽাāύে āĻ…āύেāĻ•েāχ āĻ…āύāϞাāχāύ āĻ•োāϰ্āϏ, āĻŦুāϟāĻ•্āϝাāĻŽ্āĻĒ āĻ…āĻĨāĻŦা āϏেāϞ্āĻĢ-āϞাāϰ্āύিং āĻ•āϰেāĻ“ āĻāχ āĻĒেāĻļাāϝ় āφāϏāĻ›েāύ।

⧍. āĻĒ্āϰোāĻ—্āϰাāĻŽিং āĻļেāĻ–া:

āĻĒ্āϰোāĻ—্āϰাāĻŽিং āĻĻāĻ•্āώāϤা āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰেāϰ āĻŽূāĻ–্āϝ āĻšাāϤিāϝ়াāϰ। āĻļুāϰুāϤে āύিāϚেāϰ āĻ­াāώাāĻ—ুāϞো āĻļেāĻ–া āϝেāϤে āĻĒাāϰে:

  • C/C++

  • Java

  • Python

  • JavaScript

  • SQL

ā§Š. āĻĄেāϟা āϏ্āϟ্āϰাāĻ•āϚাāϰ āĻ“ āĻ…্āϝাāϞāĻ—āϰিāĻĻāĻŽ:

āĻāϟি āĻāĻ•āϟি āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰেāϰ āĻ­িāϤ্āϤি। āĻ­াāϞো āĻ•োāĻŽ্āĻĒাāύিāĻ—ুāϞোāϤে āϚাāĻ•āϰিāϰ āχāύ্āϟাāϰāĻ­িāωāϤে āĻāĻ—ুāϞো āĻĨেāĻ•েāχ āĻĒ্āϰāĻļ্āύ āφāϏে।

ā§Ē. āĻĒ্āϰāϜেāĻ•্āϟ āϤৈāϰি:

āύিāϜ āĻšাāϤে āĻĒ্āϰāϜেāĻ•্āϟ āϤৈāϰি āĻ•āϰāϞে āĻŦাāϏ্āϤāĻŦ āĻ…āĻ­িāϜ্āĻžāϤা āĻšāϝ়। āϝেāĻŽāύ: āĻ“ā§ŸেāĻŦ āĻ…্āϝাāĻĒ, āĻŽোāĻŦাāχāϞ āĻ…্āϝাāĻĒ, āĻ—েāĻŽ, āχ-āĻ•āĻŽাāϰ্āϏ āϏাāχāϟ āχāϤ্āϝাāĻĻি।

ā§Ģ. āĻ“āĻĒেāύ āϏোāϰ্āϏ āĻ•āύ্āϟ্āϰিāĻŦিāωāĻļāύ:

GitHub, GitLab-āĻ āĻ•āύ্āϟ্āϰিāĻŦিāωāϟ āĻ•āϰāϞে āύিāϜেāϰ āϏ্āĻ•িāϞ āĻāĻŦং āĻĒ্āϰোāĻĢাāχāϞ āĻĻুāχāχ āϏāĻŽৃāĻĻ্āϧ āĻšāϝ়।

ā§Ŧ. āχāύ্āϟাāϰ্āύāĻļিāĻĒ āĻ“ āϚাāĻ•āϰি:

āĻĒ্āϰāĻĨāĻŽ āĻĻিāĻ•ে āχāύ্āϟাāϰ্āύāĻļিāĻĒেāϰ āĻŽাāϧ্āϝāĻŽে āĻŦাāϏ্āϤāĻŦ āĻ…āĻ­িāϜ্āĻžāϤা āĻ…āϰ্āϜāύ āĻ•āϰে āĻĒāϰāĻŦāϰ্āϤীāϤে āĻĢুāϞ āϟাāχāĻŽ āϚাāĻ•āϰি āύেāĻ“āϝ়া āϏāĻšāϜ āĻšāϝ়।


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিংāϝ়েāϰ āĻŦিāĻ­িāύ্āύ āĻļাāĻ–া

āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻāĻ•āϟি āĻŦিāϏ্āϤৃāϤ āĻ•্āώেāϤ্āϰ, āĻāϰ āĻŽāϧ্āϝে āϰ⧟েāĻ›ে:

  • āĻĢ্āϰāύ্āϟ-āĻāύ্āĻĄ āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ (UI/UX)

  • āĻŦ্āϝাāĻ•-āĻāύ্āĻĄ āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ (āĻĄাāϟাāĻŦেāϜ, āϏাāϰ্āĻ­াāϰ)

  • āĻĢুāϞ-āϏ্āϟ্āϝাāĻ• āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ

  • āĻŽোāĻŦাāχāϞ āĻ…্āϝাāĻĒ āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ (Android/iOS)

  • āĻ—েāĻŽ āĻĄেāĻ­েāϞāĻĒāĻŽেāύ্āϟ

  • āϏাāχāĻŦাāϰ āϏিāĻ•িāωāϰিāϟি

  • AI/ML āĻāĻŦং āĻĄেāϟা āϏাāϝ়েāύ্āϏ

  • DevOps āĻāĻŦং āĻ•্āϞাāωāĻĄ āχāĻž্āϜিāύিāϝ়াāϰিং


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰāĻĻেāϰ āϜāύ্āϝ āĻĒ্āϰ⧟োāϜāύী⧟ āϟুāϞāϏ āĻ“ āϟেāĻ•āύোāϞāϜি

  • Version Control: Git, GitHub

  • IDE/Code Editors: VS Code, IntelliJ, Eclipse

  • Frameworks: React, Angular, Django, Laravel

  • Databases: MySQL, MongoDB, PostgreSQL

  • Deployment Tools: Docker, Jenkins, AWS, Firebase


āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰ āĻšāĻ“āϝ়াāϰ āϏুāĻŦিāϧা

  • āωāϚ্āϚ āĻŦেāϤāύ āĻ“ āϚাāĻšিāĻĻা: āĻāχ āĻĒেāĻļাāϝ় āĻĻāĻ•্āώāϤা āĻ…āύুāϝাāϝ়ী āωāϚ্āϚ āĻŦেāϤāύ āĻĒাāĻ“āϝ়া āϝাāϝ়।

  • āϰিāĻŽোāϟ āĻ•াāϜেāϰ āϏুāϝোāĻ—: āϘāϰে āĻŦāϏেāχ āφāύ্āϤāϰ্āϜাāϤিāĻ• āĻ•োāĻŽ্āĻĒাāύিāϤে āĻ•াāϜ āĻ•āϰাāϰ āϏুāϝোāĻ—।

  • āϏৃāώ্āϟিāĻļীāϞāϤা: āύিāϜāϏ্āĻŦ āφāχāĻĄিāϝ়া āĻ“ āϏৃāώ্āϟিāĻļীāϞ āĻ•াāϜেāϰ āϏুāϝোāĻ— āĻĨাāĻ•ে।

  • āϏ্āϟাāϰ্āϟāφāĻĒ āĻ—āĻĄ়াāϰ āϏুāϝোāĻ—: āύিāϜেāϰ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āĻĒāĻŖ্āϝ āĻŦা āϏাāϰ্āĻ­িāϏ āĻŦাāύি⧟ে āĻŦ্āϝāĻŦāϏা āĻļুāϰু āĻ•āϰা āϏāĻŽ্āĻ­āĻŦ।


āĻŦাংāϞাāĻĻেāĻļে āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻ“ āĻ­āĻŦিāώ্āĻ¯ā§Ž

āĻŦাংāϞাāĻĻেāĻļে āĻĻিāύ āĻĻিāύ āĻāχ āĻ–াāϤ āĻŦিāϏ্āϤৃāϤ āĻšāϚ্āĻ›ে। āĻĻেāĻļেāϰ āĻŦিāĻ­িāύ্āύ āφāχāϟি āĻ•োāĻŽ্āĻĒাāύি, āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āĻĢাāϰ্āĻŽ, āĻ“ āĻĢ্āϰিāϞ্āϝাāύ্āϏিং āĻŽাāϰ্āĻ•েāϟāĻĒ্āϞেāϏে āĻāĻ–āύো āĻšাāϜাāϰ āĻšাāϜাāϰ āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰেāϰ āϚাāĻšিāĻĻা āϰāϝ়েāĻ›ে।

āϏāϰāĻ•াāϰি āωāĻĻ্āϝোāĻ— āϝেāĻŽāύ “āĻĄিāϜিāϟাāϞ āĻŦাংāϞাāĻĻেāĻļ”, āĻšাāχāϟেāĻ• āĻĒাāϰ্āĻ•, IT freelancing training āχāϤ্āϝাāĻĻিāϰ āĻ•াāϰāĻŖে āϤāϰুāĻŖāϰা āĻāĻ–āύ āφāϰāĻ“ āĻŦেāĻļি āφāĻ—্āϰāĻšী āĻšāϚ্āĻ›ে āĻāχ āĻĒেāĻļাāϝ়।

āĻ…āύেāĻ• āĻļিāĻ•্āώাāϰ্āĻĨী āĻ­াāϰ্āϚুāϝ়াāϞ āĻĒ্āϞ্āϝাāϟāĻĢāϰ্āĻŽ āϝেāĻŽāύ Coursera, Udemy, YouTube āĻĨেāĻ•ে āĻļেāĻ–াāϰ āĻŽাāϧ্āϝāĻŽে āĻāχ āĻĒেāĻļাāϝ় āϏāĻĢāϞ āĻšāϚ্āĻ›েāύ।


āϚ্āϝাāϞেāĻž্āϜ āĻ“ āĻ•āϰāĻŖীāϝ়

  • āĻĒ্āϰāϤিāϝোāĻ—িāϤা: āĻĻāĻ•্āώāϤা āĻ›াāĻĄ়া āĻļুāϧুāĻŽাāϤ্āϰ āĻĄিāĻ—্āϰি āύিāϝ়ে āϟিāĻ•ে āĻĨাāĻ•া āĻ•āĻ িāύ।

  • āĻšাāϞāύাāĻ—াāĻĻ āĻĨাāĻ•া: āĻĒ্āϰāϝুāĻ•্āϤি āĻĒ্āϰāϤিāύিāϝ়āϤ āĻĒāϰিāĻŦāϰ্āϤāύ āĻšāϚ্āĻ›ে, āϤাāχ āύিāϝ়āĻŽিāϤ āĻļেāĻ–া āφāĻŦāĻļ্āϝāĻ•।

  • āχংāϰেāϜি āĻ­াāώা āĻĻāĻ•্āώāϤা: āφāύ্āϤāϰ্āϜাāϤিāĻ• āĻ•āĻŽিāωāύিāĻ•েāĻļāύ āĻ“ āϟুāϞāϏ āĻŦ্āϝāĻŦāĻšাāϰে āχংāϰেāϜিāϰ āĻĒ্āϰāϝ়োāϜāύ āĻĒāĻĄ়ে।

āĻ•āϰāĻŖীāϝ়: āύিāϝ়āĻŽিāϤ āĻĒ্āϰ্āϝাāĻ•āϟিāϏ, āĻĒ্āϰāϜেāĻ•্āϟ āϤৈāϰি, āĻ•āύāϟেāϏ্āϟে āĻ…ংāĻļāĻ—্āϰāĻšāĻŖ, āĻ“ āϏেāϞ্āĻĢ-āϞাāϰ্āύিং āϚাāϞিāϝ়ে āϝেāϤে āĻšāĻŦে।




āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻ•েāĻŦāϞ āĻāĻ•āϟি āĻĒেāĻļা āύāϝ়, āĻāϟি āĻāĻ•āϟি āωāĻĻ্āĻ­াāĻŦāύ āĻ“ āĻĒ্āϰāϝুāĻ•্āϤিāĻ—āϤ āĻŦিāĻĒ্āϞāĻŦেāϰ āĻ…ংāĻļ। āϝাāϰা āϞāϜিāĻ•্āϝাāϞ āϚিāύ্āϤা, āϏāĻŽāϏ্āϝা āϏāĻŽাāϧাāύে āĻĒাāϰāĻĻāϰ্āĻļী āĻāĻŦং āĻĒ্āϰāϝুāĻ•্āϤি āĻ­াāϞোāĻŦাāϏেāύ — āϤাāĻĻেāϰ āϜāύ্āϝ āĻāϟি āĻāĻ•āϟি āφāĻĻāϰ্āĻļ āĻ•্āϝাāϰিāϝ়াāϰ।

āĻŦাংāϞাāĻĻেāĻļেāϰ āϤāϰুāĻŖ āϏāĻŽাāϜ āϝāĻĻি āĻāχ āĻ–াāϤে āĻĻāĻ•্āώ āĻšāϝ়ে āĻ“āĻ ে, āϤāĻŦে āĻļুāϧু āύিāϜেāĻĻেāϰ āύāϝ়, āĻĒুāϰো āĻĻেāĻļāĻ•েāχ āĻāĻ•āϟি āĻĒ্āϰāϝুāĻ•্āϤিāύিāϰ্āĻ­āϰ āϜাāϤিāϤে āϰূāĻĒাāύ্āϤāϰিāϤ āĻ•āϰা āϏāĻŽ্āĻ­āĻŦ। āϏāĻĢāϟāĻ“āϝ়্āϝাāϰ āχāĻž্āϜিāύিāϝ়াāϰিং āĻšāϚ্āĻ›ে āϏেāχ āϏ্āĻŦāĻĒ্āύেāϰ āĻĒāĻĨেāϰ āĻāĻ•āϟি āϏোāύাāϞী āϏিঁāĻĄ়ি।

IELTS Grammar Basic to Advanced (Part1 and Part2)

  📘 Part 1: Parts of Speech (āĻļāĻŦ্āĻĻেāϰ āĻĒ্āϰāĻ•াāϰāĻ­েāĻĻ) āχংāϰেāϜি āĻ­াāώা⧟ āĻŽোāϟ ā§Ž āϧāϰāύেāϰ āĻļāĻŦ্āĻĻ āφāĻ›ে āϝেāĻ—ুāϞোāĻ•ে Parts of Speech āĻŦāϞা āĻšā§Ÿ। āĻāĻ—ুāϞো āĻŦাāĻ•্āϝ āĻ—āĻ āύেāϰ āĻ­িāϤ্āϤি।...