Python Projects & Resources
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Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun
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Red:📈 Análisis del canal de Telegram Python Projects & Resources
El canal Python Projects & Resources (@pythondevelopersindia) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 63 500 suscriptores, ocupando la posición 1 987 en la categoría Tecnologías y Aplicaciones y el puesto 5 190 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 63 500 suscriptores.
Según los últimos datos del 07 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 111, y en las últimas 24 horas de 20, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.53%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 510 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 35.
- Intereses temáticos: El contenido se centra en temas clave como learning, object, module, string, loop.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Python Programming 🇮🇳
Download Free Books & Courses to master Python Programming
- ✅ Free Courses
- ✅ Projects
- ✅ Pdfs
- ✅ Bootcamps
- ✅ Notes
Admin: @Coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 08 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 08 octubre | 0 | |||
| 07 octubre | +20 | |||
| 06 octubre | +18 | |||
| 05 octubre | +8 | |||
| 04 octubre | +10 | |||
| 03 octubre | +2 | |||
| 02 octubre | 0 | |||
| 01 octubre | +13 |
Publicaciones del Canal
Sure! Here's the text with the asterisks replaced by double asterisks:
✅ Python Basics: Part-1
Data Types & Variables 🐍📚
🎯 What is a Variable?
A variable stores data in memory to be used and modified later.
Example:
name = "Alice"
age = 25
🔹 Common Python Data Types:
● String (str) – Text data
message = "Hello, World"
● Integer (int) – Whole numbers
count = 42
● Float (float) – Decimal numbers
price = 19.99
● Boolean (bool) – True or False
is_valid = True
● List (list) – Ordered, mutable sequence
fruits = ["apple", "banana", "cherry"]
● Tuple (tuple) – Ordered, immutable sequence
coords = (10.5, 20.7)
● Set (set) – Unordered collection of unique elements
colors = {"red", "green", "blue"}
● Dictionary (dict) – Key-value pairs
person = {"name": "Alice", "age": 25}
🔑 Dynamic Typing:
Python automatically detects the type, so you don’t need to declare it.
💬 Double Tap ❤️ for Part-2!
| 2 |  🎯 GigaChat 3.5 Reasoning: 5 Key Features
1️⃣ Advanced Reasoning: Explores multiple step-by-step paths, using automated verification to reinforce correct answers and self-correct
2️⃣ Autonomous Tool Usage: Independently decides when to call external APIs or revise earlier steps
3️⃣ Linear Attention: Proprietary architecture retains key context points without re-matching from scratch
4️⃣ Token Economy: Uses 37% fewer tokens than DeepSeek V4 Flash Preview on math problems
5️⃣ Proven Performance: Open-source LLM (built on GigaChat 3.5 Ultra) with massive benchmark gains:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85
🔗 MIT License. Weights on Hugging Face: fp8 | bf16 | 3 748 |
| 3 |  +9Python Interview Questions with Answers | 3 943 |
| 4 | A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊 | 4 994 |
| 5 |  🌈 2026 Job-Seeker Toolkit – Free Interview & IT Cert Resources
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Perfect for Software Developer Jobs, IT Internships, and Python Projects practice.
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Tag a friend who's job-hunting or grinding Python projects — let's ace it together! 💪
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| 6 |  📊 Pandas Cheatsheet Every Data Analyst Should Save
Pandas is one of the most important tools for data analysis. Master these core operations to work faster and more efficiently:
🔹 Read & Inspect Data
head(), shape, dtypes, describe()
🔹 Select & Filter Data
Extract relevant rows and columns with ease.
🔹 Row Selection
Use loc[] (labels) and iloc[] (positions).
🔹 Handle Missing Values
isnull(), dropna(), fillna()
🔹 Group & Aggregate
Summarize data using groupby() and aggregation functions.
🔹 Merge & Join Data
Combine datasets with merge() using different join types.
💡 Key Insight :
Strong Pandas skills help transform raw data into actionable insights faster and more effectively.
🚀 Whether you're a beginner or an experienced analyst, mastering these fundamentals is essential for data analytics success. | 5 198 |
| 7 |  Your Data Science degree just got an AI update.
Yeah.
Things are moving fast.
Python. SQL. Machine Learning. Deep Learning. MLOps.
And now GenAI, LLMs, RAG & AI-powered workflows.
An 8-month program with 20+ industry projects and live weekend classes.
Maybe Data Science was just the beginning.
https://lp.pwskills.com/data-science-ai-online-program-pw-skills?utm_source=telegram&utm_medium=influencer&utm_campaign=deepakAugDS | 5 325 |
| 8 | Today, lets understand Machine Learning in simplest way possible
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”
Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.
That’s what machine learning does — but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more ❤️ | 6 567 |
| 9 |  🚨 BREAKING: PW Skills x Microsoft just launched The Complete Live Gen AI Engineering Program
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👉 Reserve your seat now: https://pwskills.com/generative-ai/gen-ai-engineering-course-654105/?source=pwskills.com&position=course_dropdown&from=course_description | 5 845 |
| 10 | Google Free Certificate Courses — No Fees, No Experience Needed
Google offers genuinely free certificate courses across three platforms: Digital Garage, Skillshop, and Cloud Skills Boost.
Who can apply:
• Anyone with a Gmail account, no fixed eligibility criteria
• No prior experience or technical background required
• Open globally, including India
What you get:
• Google Digital Garage: Free courses on digital marketing, career development, and data skills, with certificates on completion
• Google Skillshop: 26+ official certifications in Google Ads, Analytics, and Search Ads 360, most requiring recertification every 12 months
• Google Cloud Skills Boost: 1,300+ free courses, learning pathways, and hands on labs, with completion and skill badges
• Note: Google Career Certificates on Coursera (Data Analytics, IT Support, UX Design, etc.) are not fully free, they require a paid Coursera subscription unless you qualify for financial aid or a scholarship
Documents needed: None, just a Google account
How to apply:
1. Choose your platform based on your interest: Digital Garage for marketing/career skills, Skillshop for Ads/Analytics certifications, or Cloud Skills Boost for cloud/data skills
2. Sign in with your Gmail account
3. Enroll in your chosen course
4. Complete the video lessons, quizzes, and assignments
5. Download your certificate or badge upon completion
Deadline: None, self-paced and available anytime
Apply here:
Digital Garage: https://grow.google/digitalgarage
Skillshop: https://skillshop.withgoogle.com
Cloud Skills Boost: https://cloudskillsboost.google
If you need more such type of content then do let me know by responding to this message. | 4 556 |
| 11 | ⏳ Your Python already clears half the bar.
The other half is a 60-min aptitude test - tomorrow.
Certification in AI & ML - Vishlesan i-Hub, IIT Patna ML → PyTorch → LLMs, RAG & Agents → Docker deployment ₹99 · Sunday · one attempt
🔗 https://tinyurl.com/DS-29JUL-009 | 4 147 |
| 12 | ✅ Python Exception Handling! 🐍✨
Exception handling allows your program to handle errors gracefully instead of crashing unexpectedly.
num = 10
print(num / 0)
❌ Output → ZeroDivisionError
💡 Without exception handling, the program stops immediately when an error occurs.
1. Basic Syntax:
› Use try and except to handle errors.
try:
num = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero")
✔ Output → Cannot divide by zero
2. Catch Any Exception:
Use Exception to handle all types of errors.
try:
number = int("Hello")
except Exception:
print("Something went wrong")
✔ Output → Something went wrong
3. Catch Multiple Exceptions:
try:
num = int(input("Enter a number: "))
print(10 / num)
except ValueError:
print("Invalid number")
except ZeroDivisionError:
print("Cannot divide by zero")
💡 Different errors can be handled separately.
4. Using else:
The else block runs only if no exception occurs.
try:
num = 10 / 2
except ZeroDivisionError:
print("Error")
else:
print("Division Successful")
✔ Output → Division Successful
5. Using finally:
The finally block always executes, whether an exception occurs or not.
try:
print(10 / 2)
except ZeroDivisionError:
print("Error")
finally:
print("Program Finished")
✔ Output →
5.0
Program Finished
💡 Commonly used to close files or database connections.
6. Using raise:
Manually raise an exception.
age = -5
if age < 0:
raise ValueError("Age cannot be negative")
✔ Output → ValueError: Age cannot be negative
7. Get the Error Message:
try:
print(10 / 0)
except Exception as e:
print(e)
✔ Output → division by zero
💡 e stores the actual error message.
8. Nested Exception Handling:
try:
try:
print(10 / 0)
except ZeroDivisionError:
print("Inner Exception")
except:
print("Outer Exception")
✔ Output → Inner Exception
9. Common Python Exceptions:
✔ ZeroDivisionError → Dividing by zero: 10 / 0
✔ ValueError → Invalid value: int("Hello")
✔ TypeError → Invalid data type: 10 + "20"
✔ IndexError → Invalid list index:
nums = [1, 2]
print(nums[5])
✔ KeyError → Missing dictionary key:
student = {"name": "Alex"}
print(student["age"])
✔ FileNotFoundError → File doesn't exist: open("data.txt")
10. Practice Examples:
✔ Handle invalid input
try:
age = int(input("Enter age: "))
print(age)
except ValueError:
print("Please enter a valid number")
✔ Handle list index error
try:
nums = [10, 20]
print(nums[5])
except IndexError:
print("Index out of range")
✔ Handle dictionary key error
try:
student = {"name": "Alex"}
print(student["age"])
except KeyError:
print("Key not found")
💡 Exception handling makes your programs more reliable by preventing unexpected crashes and providing meaningful error messages.
💬 Tap ❤️ if this helped you! | 4 220 |
| 13 |  You already know Python.
That’s 20% of an AI career. Here’s the other 80%.
ML with Scikit-learn & XGBoost → Deep Learning with PyTorch → LLMs, RAG & AI Agents → Deployment with Docker.
That’s the exact roadmap of the Certification in AI & ML - Vishlesan i-Hub, IIT Patna.
✅ 9 Months | Online | IIT faculty & industry mentors
✅ Ship deployed projects: churn predictor, image classifier + capstone
✅ Placement support through Masai's network of 5000+ companies
Your Python already clears half the entry bar. The rest is a ₹99 test this Sunday.
🗓 2nd August - slot booking closing soon
🔗 https://tinyurl.com/DS-29JUL-009 | 3 651 |
| 14 | Last 6 Hours Remaining!
Before the application closes for E&ICT IIT Roorkee AI & ML Program.
Don't miss out on the chance to:
• Learn live from IIT professors & industry experts
• Build real AI projects
• Get Placement Support from Masai.
Register NOW | 4 294 |
| 15 |  You already know Python.
Now learn how companies actually use it for AI.
Applications are open for TiHAN IIT Hyderabad's AI & ML Program.
✅ Learn from TiHAN scientists, IIT professors & industry experts
✅ Build projects from Flipkart & Mamaearth
✅ Assured interview at TiHAN IIT Hyderabad with 9+ CGPA
✅ Placement support across 5000+ companies through Masai
🗓 Entrance Exam: 19th July
🔗 Register: https://tinyurl.com/DS-26Jul-009 | 4 569 |
| 16 | List of Python Project Ideas💡👨🏻💻🐍 -
Beginner Projects
🔹 Calculator
🔹 To-Do List
🔹 Number Guessing Game
🔹 Basic Web Scraper
🔹 Password Generator
🔹 Flashcard Quizzer
🔹 Simple Chatbot
🔹 Weather App
🔹 Unit Converter
🔹 Rock-Paper-Scissors Game
Intermediate Projects
🔸 Personal Diary
🔸 Web Scraping Tool
🔸 Expense Tracker
🔸 Flask Blog
🔸 Image Gallery
🔸 Chat Application
🔸 API Wrapper
🔸 Markdown to HTML Converter
🔸 Command-Line Pomodoro Timer
🔸 Basic Game with Pygame
Advanced Projects
🔺 Social Media Dashboard
🔺 Machine Learning Model
🔺 Data Visualization Tool
🔺 Portfolio Website
🔺 Blockchain Simulation
🔺 Chatbot with NLP
🔺 Multi-user Blog Platform
🔺 Automated Web Tester
🔺 File Organizer | 4 254 |
| 17 | ✔ Print all values
print(student.values())
✔ Add a new key
student["country"] = "India"
print(student)
✔ Update a value
student["age"] = 23
print(student)
💡 Dictionaries are one of the most powerful data structures in Python and are widely used to store structured data like JSON, APIs, and database records.
💬 Tap ❤️ if this helped you learn Python faster!
-----
1.32 ₽ · /balance_help | 4 108 |
| 18 | ✅ Python Dictionaries! 🐍✨
Dictionaries are used to store data in key-value pairs. They are ordered, mutable, and do not allow duplicate keys.
student = {
"name": "Alex",
"age": 22,
"city": "Mumbai"
}
1. Basic Syntax:
› Dictionaries use curly braces {}.
› Each item consists of a key: value pair.
person = {
"name": "John",
"age": 25
}
💡 Keys must be unique, but values can be duplicated.
2. Access Dictionary Values:
Access values using their keys.
student = {
"name": "Alex",
"age": 22
}
print(student["name"])
print(student["age"])
✔ Output
Alex
22
3. Using get() Method:
Safely access a value without getting an error if the key doesn't exist.
student = {
"name": "Alex",
"age": 22
}
print(student.get("name"))
✔ Output
Alex
💡 If the key doesn't exist, get() returns None by default.
4. Change Dictionary Values:
student = {
"name": "Alex",
"age": 22
}
student["age"] = 23
print(student)
✔ Output
{'name': 'Alex', 'age': 23}
5. Add New Items:
student = {
"name": "Alex"
}
student["city"] = "Mumbai"
print(student)
✔ Output
{'name': 'Alex', 'city': 'Mumbai'}
6. Remove Items:
Using pop()
student.pop("age")
Using del
del student["city"]
Remove all items
student.clear()
7. Dictionary Length:
student = {
"name": "Alex",
"age": 22
}
print(len(student))
✔ Output
2
8. Loop Through a Dictionary:
Loop through keys
for key in student:
print(key)
✔ Output
name
age
Loop through values
for value in student.values():
print(value)
✔ Output
Alex
22
Loop through key-value pairs
for key, value in student.items():
print(key, value)
✔ Output
name Alex
age 22
9. Check if a Key Exists:
student = {
"name": "Alex",
"age": 22
}
print("name" in student)
✔ Output
True
10. Common Dictionary Methods:
✔ keys() → Returns all keys
print(student.keys())
✔ values() → Returns all values
print(student.values())
✔ items() → Returns key-value pairs
print(student.items())
✔ update() → Updates dictionary
student.update({"age": 24})
✔ Output
{'name': 'Alex', 'age': 24}
11. Nested Dictionaries:
students = {
"student1": {
"name": "Alex",
"age": 22
},
"student2": {
"name": "John",
"age": 25
}
}
print(students["student1"]["name"])
✔ Output
Alex
12. Practice Examples:
✔ Print all keys
student = {
"name": "Alex",
"age": 22
}
print(student.keys()) | 3 783 |
| 19 | Final 6 Hours Left!
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| 20 |  You already know Python.
Now learn how companies actually use it for AI.
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