ar
Feedback

لا تقع ضحية للمخادعين! تيليمتريو يكتشف ويُميّز هذه القنوات 👉 إذا كنت تريد رؤية العلامة، اشترك

Python Projects & Resources

Python Projects & Resources

الذهاب إلى القناة على Telegram

Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Python Projects & Resources

تُعد قناة Python Projects & Resources (@pythondevelopersindia) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 63 322 مشتركاً، محتلاً المرتبة 2 048 في فئة التكنولوجيات والتطبيقات والمرتبة 5 088 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 63 322 مشتركاً.

بحسب آخر البيانات بتاريخ 10 نوفمبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -166، وفي آخر 24 ساعة بمقدار 10، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 5.99‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.23‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 3 794 مشاهدة. وخلال اليوم الأول يجمع عادةً 781 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 13.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, object, module, string, loop.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 11 نوفمبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

63 322
المشتركون
+1024 ساعات
-597 أيام
-16630 أيام
جذب المشتركين
نوفمبر '26
نوفمبر '26
+48
في 0 قنوات
أكتوبر '26
+196
في 3 قنوات
Get PRO
سبتمبر '26
+222
في 5 قنوات
Get PRO
أغسطس '26
+399
في 7 قنوات
Get PRO
يوليو '26
+479
في 10 قنوات
Get PRO
يونيو '26
+363
في 6 قنوات
Get PRO
مايو '26
+479
في 8 قنوات
Get PRO
أبريل '26
+396
في 9 قنوات
Get PRO
مارس '26
+261
في 7 قنوات
Get PRO
فبراير '26
+781
في 11 قنوات
Get PRO
يناير '26
+936
في 11 قنوات
Get PRO
ديسمبر '25
+882
في 2 قنوات
Get PRO
نوفمبر '25
+1 113
في 6 قنوات
Get PRO
أكتوبر '25
+966
في 9 قنوات
Get PRO
سبتمبر '25
+1 206
في 14 قنوات
Get PRO
أغسطس '25
+1 294
في 30 قنوات
Get PRO
يوليو '25
+1 200
في 33 قنوات
Get PRO
يونيو '25
+1 458
في 36 قنوات
Get PRO
مايو '25
+3 544
في 33 قنوات
Get PRO
أبريل '25
+5 846
في 33 قنوات
Get PRO
مارس '25
+2 109
في 34 قنوات
Get PRO
فبراير '25
+1 437
في 26 قنوات
Get PRO
يناير '25
+1 887
في 37 قنوات
Get PRO
ديسمبر '24
+824
في 13 قنوات
Get PRO
نوفمبر '24
+1 470
في 16 قنوات
Get PRO
أكتوبر '24
+1 518
في 18 قنوات
Get PRO
سبتمبر '24
+1 933
في 12 قنوات
Get PRO
أغسطس '24
+2 152
في 9 قنوات
Get PRO
يوليو '24
+5 254
في 15 قنوات
Get PRO
يونيو '24
+3 642
في 13 قنوات
Get PRO
مايو '24
+1 910
في 17 قنوات
Get PRO
أبريل '24
+3 971
في 15 قنوات
Get PRO
مارس '24
+2 368
في 11 قنوات
Get PRO
فبراير '24
+4 090
في 5 قنوات
Get PRO
يناير '24
+2 023
في 3 قنوات
Get PRO
ديسمبر '23
+2 242
في 5 قنوات
Get PRO
نوفمبر '23
+670
في 10 قنوات
Get PRO
أكتوبر '23
+298
في 3 قنوات
Get PRO
سبتمبر '23
+512
في 0 قنوات
Get PRO
أغسطس '23
+776
في 0 قنوات
Get PRO
يوليو '23
+866
في 0 قنوات
Get PRO
يونيو '23
+43
في 0 قنوات
Get PRO
مايو '23
+226
في 0 قنوات
Get PRO
أبريل '23
+269
في 0 قنوات
Get PRO
مارس '23
+188
في 0 قنوات
Get PRO
فبراير '23
+85
في 0 قنوات
Get PRO
يناير '23
+256
في 0 قنوات
Get PRO
ديسمبر '22
+171
في 0 قنوات
Get PRO
نوفمبر '22
+230
في 0 قنوات
Get PRO
أكتوبر '22
+213
في 0 قنوات
Get PRO
سبتمبر '22
+205
في 0 قنوات
Get PRO
أغسطس '22
+217
في 0 قنوات
Get PRO
يوليو '22
+187
في 0 قنوات
Get PRO
يونيو '22
+281
في 0 قنوات
Get PRO
مايو '22
+361
في 0 قنوات
Get PRO
أبريل '22
+92
في 0 قنوات
Get PRO
مارس '22
+241
في 0 قنوات
Get PRO
فبراير '22
+280
في 0 قنوات
Get PRO
يناير '22
+370
في 0 قنوات
Get PRO
ديسمبر '21
+96
في 0 قنوات
Get PRO
نوفمبر '21
+55
في 0 قنوات
Get PRO
أكتوبر '21
+89
في 0 قنوات
Get PRO
سبتمبر '21
+711
في 0 قنوات
التاريخ
نمو المشتركين
الإشارات
القنوات
11 نوفمبر0
10 نوفمبر+11
09 نوفمبر0
08 نوفمبر0
07 نوفمبر+1
06 نوفمبر+2
05 نوفمبر+6
04 نوفمبر+4
03 نوفمبر+2
02 نوفمبر+14
01 نوفمبر+8
منشورات القناة
🐍 PYTHON INTERVIEW SERIES – PART 1 Hi guys! Let's start our Python Interview Preparation Series today, covering the most important questions frequently asked in interviews. We'll start with the basics and gradually move towards intermediate and advanced-level questions. Let's get started! Q1. What is Python? Python is a high-level, interpreted, general-purpose programming language known for its simple syntax and readability. Q2. What are the main features of Python? - Easy to learn and understand. - Interpreted language. - Dynamically typed. - Object-oriented programming support. - Open-source and free. - Large collection of libraries. - Cross-platform compatibility. Q3. Is Python compiled or interpreted? Python is generally considered an interpreted language. In CPython, source code is first compiled into bytecode, which is then executed by the Python Virtual Machine (PVM). Q4. What is a variable in Python? A variable is a name used to store or refer to a value. Example:
name = "John"
age = 25

print(name)
print(age)
Q5. What are the different data types in Python? Python has several built-in data types: - int – Integer - float – Decimal numbers - str – Strings - bool – Boolean values - list – Ordered, mutable collection - tuple – Ordered, immutable collection - set – Collection of unique elements - dict – Key-value pairs - complex – Complex numbers Q6. What is the difference between a list and a tuple? List: - Mutable (can be modified). - Uses square brackets []. - Supports adding and removing elements. Tuple: - Immutable (cannot be modified). - Uses parentheses (). - Does not support item reassignment. Q7. What is the difference between == and is in Python? - == checks whether two objects have equal values. - is checks whether two references point to the same object. Example:
a = [1, 2, 3]
b = [1, 2, 3]

print(a == b)  # True
print(a is b)  # False
Q8. What is indentation in Python? Indentation refers to the spaces at the beginning of a code line. Python uses indentation to define blocks of code. Example:
age = 20

if age >= 18:
    print("Eligible to vote")
Q9. What is the difference between mutable and immutable data types? Mutable: Objects whose values can be changed after creation. Examples: List, Dictionary, Set. Immutable: Objects whose values cannot be changed after creation. Examples: Integer, Float, String, Tuple, Boolean. Q10. How do you take input from a user in Python? Python uses the input() function to accept user input. Example:
name = input("Enter your name: ")
print("Hello", name)
Remember: The input() function returns a string by default. Double Tap ❤️ for Part 2!

2
+4
لا يوجد نص...
2 241
3
Python Cheat sheet
2 175
4
10 Steps to Landing a High Paying Job in Data Analytics 1. Learn SQL - joins & windowing functions is most important 2. Learn Excel- pivoting, lookup, vba, macros is must 3. Learn Dashboarding on POWER BI/ Tableau 4. ⁠Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries 5. ⁠Know basics of descriptive statistics 6. ⁠With AI/ copilot integrated in every tool, know how to use it and add to your projects 7. ⁠Have hands on any 1 cloud platform- AZURE/AWS/GCP 8. ⁠WORK on atleast 2 end to end projects and create a portfolio of it 9. ⁠Prepare an ATS friendly resume & start applying 10. ⁠Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those. Give more interview to boost your chances through consistent practice & feedback 😄👍
3 408
5
✈️ Flight cancelled. Connection missed. What does your AI agent do? Build the agent that checks flights, reads the policy and
✈️ Flight cancelled. Connection missed. What does your AI agent do? Build the agent that checks flights, reads the policy and recommends the right option. That's one of the capstone projects in the Certification in Agentic AI by Vishlesan i-Hub, IIT Patna. ✅ 4 Months | Online | 10 hrs/week | Live classes ✅ 6+ course projects, 15+ tools ✅ Learn Python, RAG, LangChain, LangGraph & FastAPI ✅ Placement assistance for 1 year, plus resume review & interview coaching by Masai* 📅 Qualifier Test: 25th Oct Test fee: ₹29 🔗 https://tinyurl.com/aff-iitp-agai *Eligibility: 7+ CGPA and 65% attendance
4 271
6
🐍 Build Video Generation Apps with Kandinsky 6.0 Video Kandinsky 6.0 Video is out — an open-source lineup (MIT license) for
🐍 Build Video Generation Apps with Kandinsky 6.0 Video   Kandinsky 6.0 Video is out — an open-source lineup (MIT license) for creating videos with synchronized sound: the flagship Pro (29B) and the lightweight Lite (3B), which runs even on consumer GPUs. Perfect for Python developers building creative applications.   💡 Project ideas: • Content creation platform • Educational video generator • Marketing automation tool • ASMR content creator • Meme animator   🔧 Integration guide: The model works with popular Python libraries:   # Available on: - Diffusers - FastVideo  - ComfyUI - SGLang - vLLM-omni   No need to write integration from scratch — plug directly into existing products.   📋 Technical specs: • Input: text prompt or first frame + audio description • Output: Full HD video with synchronized sound • Duration: up to 5 seconds • Audio: 44 kHz quality • Features: lip-sync, realistic physics, ambient sounds • GPU: Lite runs on 16–32 GB VRAM (RTX 5060 Ti to 5090)   📦 License: MIT (free for commercial use)   🎯 Use cases: • Social media content • Product demonstrations • Educational materials • Creative prototyping   🔗 Hugging Face
4 735
7
🚀 Python Basics Part 2 🐍🔥 Control Flow in Python Now that you know Python basics, it’s time to make programs smarter using conditions and loops. 💻 In this part, you’ll learn: ✅ if-else Statements ✅ Loops ✅ break & continue ✅ Nested Conditions 🧠 1. if Statement in Python "if" is used to check conditions. Example: age = 18 if age >= 18: print("Eligible") 📌 Output: Eligible ✔ The code runs only if the condition is True. 🔀 2. if-else Statement Used when there are two possible outcomes. Example: number = 5 if number % 2 == 0: print("Even Number") else: print("Odd Number") 📌 Output: Odd Number ⚡ 3. if-elif-else Ladder Used for multiple conditions. Example: marks = 75 if marks >= 90: print("Grade A") elif marks >= 70: print("Grade B") else: print("Grade C") 📌 Output: Grade B 🔁 4. for Loop in Python "for" loop repeats code multiple times. Example: for i in range(5): print(i) 📌 Output: 0 1 2 3 4 ✔ "range(5)" generates numbers from 0 to 4. 🔄 5. while Loop in Python "while" loop runs until a condition becomes False. Example: count = 1 while count <= 5: print(count) count += 1 📌 Output: 1 2 3 4 5 ⛔ 6. break Statement "break" stops the loop immediately. Example: for i in range(10): if i == 5: break print(i) 📌 Output: 0 1 2 3 4 ⏭ 7. continue Statement "continue" skips the current iteration. Example: for i in range(5): if i == 2: continue print(i) 📌 Output: 0 1 3 4 🧩 8. Nested Conditions Conditions inside another condition. Example: age = 20 has_id = True if age >= 18: if has_id: print("Entry Allowed") 📌 Output: Entry Allowed 🛠 Practice Programs ✅ Even/Odd Checker ✅ Multiplication Table ✅ Number Guessing Game ✅ Countdown Timer ✅ Grade Calculator 🔥 Common Beginner Mistakes ❌ Wrong indentation ❌ Infinite loops ❌ Forgetting ":" after conditions ❌ Using "=" instead of "==" 💡 Pro Tip ✔ First understand logic ✔ Then write syntax Programming is more about thinking than memorizing. 📅 What to Learn Next? ➡ Functions ➡ Lambda Functions ➡ Recursion ➡ Modules & Imports 📌 Double Tap ❤️ For Part-3 ----- 1.32 ₽ · /balance_help
3 954
8
📢 Advertising in this channel You can place an ad via Telega․io. It takes just a few minutes. Formats and current rates: Vie
📢 Advertising in this channel You can place an ad via Telega․io. It takes just a few minutes. Formats and current rates: View details
953
9
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!
5 714
10
🎯 GigaChat 3.5 Reasoning: 5 Key Features 1️⃣ Advanced Reasoning: Explores multiple step-by-step paths, using automated verif
🎯 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
5 194
11
Python Interview Questions with Answers+9
Python Interview Questions with Answers
4 552
12
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 😊
4 713
13
🌈 2026 Job-Seeker Toolkit – Free Interview & IT Cert Resources 🔥The 2026 hiring market is shifting fast. We've put together
🌈 2026 Job-Seeker Toolkit – Free Interview & IT Cert Resources 🔥The 2026 hiring market is shifting fast. We've put together a 100% free resource bundle covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity — including: ✅Q&A banks & mock exams ✅Behavioral interview guides ✅Technical deep-dives for coding & infrastructure roles ✅Real-world project scenarios Perfect for Software Developer Jobs, IT Internships, and Python Projects practice. 🎯 Interview Question Bank → https://bit.ly/3UP1fah 🪜 Online Free Course For Python & Excel→ https://bit.ly/4zJXCmh 📘 Free Cert E‑Book → https://bit.ly/4zLyDip ☁️ Free AI Materials → https://bit.ly/3Utk3vM 📊 Cloud Study Guide → https://bit.ly/4zO1ORT 🧠 Free Mock Exam → https://bit.ly/4gyN3ta Tag a friend who's job-hunting or grinding Python projects — let's ace it together! 💪 🧠 Join Study Community:  https://chat.whatsapp.com/FQOG04r9xSiIa2ElhaNUJU ❄️ 1-on-1 support: https://wa.link/fp3nd9
4 078
14
📊 Pandas Cheatsheet Every Data Analyst Should Save Pandas is one of the most important tools for data analysis. Master these
📊 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
15
Your Data Science degree just got an AI update. Yeah. Things are moving fast. Python. SQL. Machine Learning. Deep Learning. M
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
16
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
17
🚨 BREAKING: PW Skills x Microsoft just launched The Complete Live Gen AI Engineering Program Generative AI isn't the future
🚨 BREAKING: PW Skills x Microsoft just launched The Complete Live Gen AI Engineering Program Generative AI isn't the future anymore, it's the present. And now you can master it live, with Microsoft's backing behind you. Learn Agentic AI, LLMOps & real-world AI Development, taught through live interactive classes, in Hinglish, over a structured 5-month journey. 🎓 Bonus: Includes a Premium Microsoft Module, added credibility, added skills, added career value. 🎁 Use code GENAI20 and get 20% OFF instantly. 💰 Starting at just ₹4,999. 📅 Batch starts 20th August 2026, seats are limited, and this launch price won't last. Don't just watch the AI wave. Build it. 👉 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
18
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
19
⏳ 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
20
✅ 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