Artificial Intelligence & ChatGPT Prompts
Kanalga Telegram’da o‘tish
🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Artificial Intelligence & ChatGPT Prompts analitikasi
Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 42 064 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 192-o'rinni va Hindiston mintaqasida 8 992-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 42 064 obunachiga ega bo‘ldi.
27 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -135 ga, so‘nggi 24 soatda esa -10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.08% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.60% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 877 marta ko‘riladi; birinchi sutkada odatda 252 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, algorithm, detection, llm, pattern kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“🔓Unlock Your Coding Potential with ChatGPT
🚀 Your Ultimate Guide to Ace Coding Interviews!
💻 Coding tips, practice questions, and expert advice to land your dream tech job.
For Promotions: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 28 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
42 064
Obunachilar
-1024 soatlar
-557 kun
-13530 kun
Postlar arxiv
SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.
Let’s dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
#sql
𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗕𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 😍
👉Learn from IIT faculty and industry experts
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Evolution of Storage Devices📋
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#techinfo
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗢𝗳𝗳𝗲𝗿𝗲𝗱 𝗕𝘆 𝗜𝗜𝗧'𝘀 & 𝗜𝗜𝗠 😍
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𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 :- https://pdlink.in/4rMivIA
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗪𝗶𝘁𝗵 𝗔𝗜 :- https://pdlink.in/4ay4wPG
𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗪𝗶𝘁𝗵 𝗔𝗜 :- https://pdlink.in/3ZtIZm9
𝗠𝗟 𝗪𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3OD9jI1
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✅ Free Resources to Learn SQL in 2025 🧠📚
1. YouTube Channels
• freeCodeCamp – Comprehensive SQL courses
• Simplilearn – SQL basics and advanced topics
• CodeWithMosh – SQL tutorial for beginners
• Alex The Analyst – Practical SQL for data analysis
2. Websites
• W3Schools SQL Tutorial – Easy-to-understand basics
• SQLZoo – Interactive SQL tutorials with exercises
• GeeksforGeeks SQL – Concepts, interview questions, and examples
• LearnSQL – Free courses and interactive editor
3. Practice Platforms
• LeetCode (SQL section) – Interview-style SQL problems
• HackerRank (SQL section) – Challenges and practice problems
• StrataScratch – Real-world SQL questions from companies
• SQL Fiddle – Online SQL sandbox for testing queries
4. Free Courses
• Khan Academy: Intro to SQL – Basic database concepts and SQL
• Codecademy: Learn SQL (Basic) – Interactive lessons
• Great Learning: SQL for Beginners – Free certification course
• Udemy (search for free courses) – Many introductory SQL courses often available for free
5. Books for Starters
• “SQL in 10 Minutes, Sams Teach Yourself” – Ben Forta
• “SQL Practice Problems: 57 Problems to Test Your SQL Skills” – Sylvia Moestl Wasserman
• “Learning SQL” – Alan Beaulieu
6. Must-Know Concepts
• SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY
• JOINs (INNER, LEFT, RIGHT, FULL)
• Subqueries, CTEs (Common Table Expressions)
• Window Functions (RANK, ROW_NUMBER, LEAD, LAG)
• Basic DDL (CREATE TABLE) and DML (INSERT, UPDATE, DELETE)
💡 Practice consistently with real-world scenarios.
💬 Tap ❤️ for more!
𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 😍
𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗱𝗶𝗻𝗴 & 𝗚𝗲𝘁 𝗣𝗹𝗮𝗰𝗲𝗱 𝗜𝗻 𝗧𝗼𝗽 𝗠𝗡𝗖𝘀
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Anthropic accused Deepseek for stealing data
But, is this me? 🤔
Or does it feel like every LLM ends up being accused of “stealing data” at some point?
𝗔𝗜 & 𝗠𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗕𝘆 𝗜𝗜𝗧 𝗣𝗮𝘁𝗻𝗮 😍
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𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗦𝗰𝗵𝗼𝗹𝗮𝗿𝘀𝗵𝗶𝗽 𝗧𝗲𝘀𝘁 👇 :-
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Master Python programming in 15 days with Free Resources 😄👇
Days 1-3: Introduction to Python
- Day 1: Start by installing Python on your computer.
- Day 2: Learn the basic syntax and data types in Python (variables, numbers, strings).
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Days 4-6: Control Structures
- Day 4: Understand conditional statements (if, elif, else).
- Day 5: Learn about loops (for and while) and iterators.
- Day 6: Work on small projects to practice using conditionals and loops.
Days 7-9: Data Structures
- Day 7: Learn about lists and how to manipulate them.
- Day 8: Explore dictionaries and sets.
- Day 9: Understand tuples and lists comprehensions.
Days 10-12: Functions and Modules
- Day 10: Learn how to define functions in Python.
- Day 11: Understand scope and global vs. local variables.
- Day 12: Explore Python's module system and create your own modules.
Days 13-15: Intermediate Concepts
- Day 13: Work with file handling and I/O operations.
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FREE RESOURCES TO LEARN PYTHON 👇
Microsoft course for Python: https://learn.microsoft.com/en-us/training/paths/beginner-python/
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🎓 𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 – 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗧𝗶𝗺𝗲! 😍
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✅ Data Science Project Series: Part 1 - Loan Prediction.
Project goal
Predict loan approval using applicant data.
Business value
- Faster decisions
- Lower default risk
- Clear interview story
Dataset
Use the common Loan Prediction dataset from analytics practice platforms.
Target
Loan_Status
Y approved
N rejected
Tech stack
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
Step 1. Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
df = pd.read_csv("loan_prediction.csv")
df.head()
df.shape
df.info()
df.isnull().sum()
df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)
df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0], inplace=True)
df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)
categorical_cols = ['Gender','Married','Dependents','Self_Employed']
for col in categorical_cols:
df[col].fillna(df[col].mode()[0], inplace=True)
sns.countplot(x='Credit_History', hue='Loan_Status', data=df)
plt.show()
Income distribution.python
sns.histplot(df['ApplicantIncome'], kde=True)
plt.show()
df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']
# Log transform loan amount
df['LoanAmount_log'] = np.log(df['LoanAmount'])
le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
df[col] = le.fit_transform(df[col])
X = df.drop('Loan_Status', axis=1)
y = df['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
confusion_matrix(y_test, y_pred)
Classification report.python
print(classification_report(y_test, y_pred))
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗔𝗿𝗲 𝗛𝗶𝗴𝗵𝗹𝘆 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗜𝗻 𝟮𝟬𝟮𝟲😍
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🧠 10 Mindset Shifts to Succeed in Programming & AI 🚀💻
1️⃣ Learn by Building
→ Don’t just watch tutorials—create projects, even small ones. Practice beats theory.
2️⃣ Fail Fast, Learn Faster
→ Bugs and errors are part of the process. Debugging teaches more than smooth runs.
3️⃣ Think in Systems, Not Scripts
→ Build reusable, modular systems instead of one-time scripts.
4️⃣ Start with Logic, Then Code
→ Don’t jump into code. Understand the logic, sketch it out first.
5️⃣ Embrace the AI Toolkit
→ Use tools like ChatGPT, Copilot, LangChain—they boost your output, not replace you.
6️⃣ Read Source Code
→ Understand how libraries and tools work internally—it sharpens your skills.
7️⃣ Communicate Clearly
→ Great programmers explain problems, solutions, and code simply—write clean code & good docs.
8️⃣ Consistency > Intensity
→ Daily learning or coding (even 30 mins) compounds over time.
9️⃣ Ask Better Questions
→ Whether in forums or AI prompts, clarity in your question leads to better answers.
🔟 Stay Curious, Stay Humble
→ Tech changes fast. Stay open to learning and unlearning.
💬 Double Tap ❤️ for more!
𝗔𝗜 & 𝗠𝗟 𝗔𝗿𝗲 𝗔𝗺𝗼𝗻𝗴 𝘁𝗵𝗲 𝗧𝗼𝗽 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝗗𝗲𝗺𝗮𝗻𝗱!😍
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🚀 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 | 𝗚𝗼𝘃𝘁 𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱😍
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𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴:- https://pdlink.in/3LoutZd
𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:- https://pdlink.in/3N9VOyW
𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:- https://pdlink.in/4qgtrxU
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Top 10 Python Concepts
Variables & Data Types
Understand integers, floats, strings, booleans, lists, tuples, sets, and dictionaries.
Control Flow (if, else, elif)
Write logic-based programs using conditional statements.
Loops (for & while)
Automate tasks and iterate over data efficiently.
Functions
Build reusable code blocks with def, understand parameters, return values, and scope.
List Comprehensions
Create and transform lists concisely:
[x*2 for x in range(10) if x % 2 == 0]
Modules & Packages
Import built-in, third-party, or custom modules to structure your code.
Exception Handling
Handle errors using try, except, finally for robust programs.
Object-Oriented Programming (OOP)
Learn classes, objects, inheritance, encapsulation, and polymorphism.
File Handling
Open, read, write, and manage files using open(), read(), write().
Working with Libraries
Use powerful libraries like:
- NumPy for numerical operations
- Pandas for data analysis
- Matplotlib/Seaborn for visualization
- Requests for API calls
- JSON for data parsing
#python
