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Python Interview Projects & Free Courses Admin: @Coderfun

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📈 Аналітичний огляд Telegram-каналу Python Projects & Free Books

Канал Python Projects & Free Books (@pythonfreebootcamp) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 40 908 підписників, посідаючи 3 337 місце в категорії Технології та додатки та 10 047 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 40 908 підписників.

За останніми даними від 05 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 175, а за останні 24 години на 29, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 4.03%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.77% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 1 649 переглядів. Протягом першої доби публікація в середньому набирає 314 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, analyst, framework, link:-, structure.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
Python Interview Projects & Free Courses Admin: @Coderfun

Завдяки високій частоті оновлень (останні дані отримано 06 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

40 908
Підписники
+2924 години
+517 днів
+17530 день
Архів дописів
Pie chart using Seaborn
Pie chart using Seaborn

Pie chart using Plotly
Pie chart using Plotly

Pie chart using Matplotlib
Pie chart using Matplotlib

Pie chart plot using Python - Data Science

𝗧𝗼𝗽 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍 Python is one of the most versatile and in-demand pro
𝗧𝗼𝗽 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍 Python is one of the most versatile and in-demand programming languages today. Whether you’re a beginner or looking to refresh your coding skills, these beginner-friendly courses will guide you step by step. 𝗟𝗲𝗮𝗿𝗻 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- https://pdlink.in/4gG4k2q All The Best 🎉

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Complete Roadmap to Learn Python Programming in 2025 Beginner Level 1. Basics of Python - Understanding syntax and basic concepts - Variables and data types - Basic operators and expressions - Input and output functions - Conditional statements (if, elif, else) - Loops (for, while) 2. Data Structures - Lists - Tuples - Sets - Dictionaries 3. Functions and Modules - Defining and calling functions - Arguments and return values - Lambda functions - Built-in modules and importing external modules Intermediate Level 4. File Handling - Reading from and writing to files - Working with CSV, JSON, and other file formats 5. Object-Oriented Programming (OOP) - Classes and objects - Methods and constructors - Inheritance and polymorphism - Encapsulation and abstraction 6. Error Handling and Exceptions - Try, except, finally blocks - Raising exceptions - Custom exceptions 7. Libraries and Frameworks - Understanding and using popular libraries (NumPy, Pandas, Matplotlib) - Introduction to web frameworks (Flask, Django) Advanced Level 8. Advanced Concepts - Decorators - Generators - Context managers 9. Working with Databases - SQL and NoSQL databases - ORM (Object-Relational Mapping) with SQLAlchemy or Django ORM 10. Web Development - Full-stack development with Django or Flask - RESTful APIs and backend services 11. Data Science and Machine Learning - Data analysis with Pandas - Data visualization with Matplotlib and Seaborn - Machine learning with Scikit-Learn and TensorFlow Tools and Best Practices 12. Version Control - Using Git and GitHub for version control - Collaboration and branching strategies 13. Testing and Debugging - Unit testing with Unittest or PyTest - Debugging techniques and tools 14. Development Environment - Setting up IDEs (PyCharm, VS Code) - Virtual environments and dependency management 15. Code Quality - Writing clean and efficient code - Adhering to PEP 8 standards - Code reviews and refactoring Best Resource to learn Python Python Interview Questions with Answers Freecodecamp Python ML Course with FREE Certificate Python for Data Analysis Python course for beginners by Microsoft Scientific Computing with Python Python course by Google Python Free Resources Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍

𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀𝗲𝘁 😍 ✅ Artificial Intelligence – Master AI & Mac
𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀𝗲𝘁 😍 ✅ Artificial Intelligence – Master AI & Machine Learning ✅ Blockchain – Understand decentralization & smart contracts💰 ✅ Cloud Computing – Learn AWS, Azure&cloud infrastructure ☁ ✅ Web 3.0 – Explore the future of the Internet &Apps 🌐 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/4aM1QO0 Enroll For FREE & Get Certified 🎓

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing!

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 - Artificial Intelligence for Beginners - Data Scien
𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 - Artificial Intelligence for Beginners - Data Science for Beginners - Machine Learning for Beginners   𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40OgK1w Enroll For FREE & Get Certified 🎓

Python from scratch by University of Waterloo 0. Introduction 1. First steps 2. Built-in functions 3. Storing and using information 4. Creating functions 5. Booleans 6. Branching 7. Building better programs 8. Iteration using while 9. Storing elements in a sequence 10. Iteration using for 11. Bundling information into objects 12. Structuring data 13. Recursion https://open.cs.uwaterloo.ca/python-from-scratch/ #python

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗔𝗜 & 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗜𝗕𝗠!😍 Want to break into t
𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗔𝗜 & 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗜𝗕𝗠!😍 Want to break into tech or level up your skills?💡 ✅ Data Analytics: Analyze & visualize data like a pro ✅ Python: The most in-demand programming language ✅ AI & Machine Learning: Build smart applications ✅ SQL: Work with databases & extract insights 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/40F7YTD 🔥 Start your journey today!

Python Iterators 👆
Python Iterators 👆

𝗧𝗼𝗽 𝟱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Data Science Foundations 2)SQL for
𝗧𝗼𝗽 𝟱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Data Science Foundations 2)SQL for Data Science 3)Python for Data Science 4)Introduction to Data Science 5)Data Science Projects  𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/4hDFv7E Enroll For FREE & Get Certified 🎓

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𝗧𝗮𝘁𝗮 𝗚𝗿𝗼𝘂𝗽 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍 TCS plans to hire 40,000 trainees in 2025, here are these 3 virtual internships by Tata Group that you can take which will take roughly 4-6 hours to complete. After completing this internship you will get a free certificate that you can add in your resume which will help to increase your chances of getting hired.  𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40Ej1MM Enroll For FREE & Get Certified 🎓

⌨️ Piechart using matplotlib in Python
⌨️ Piechart using matplotlib in Python

𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗶𝘁𝗶 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 😍 🚀 100% Free – No hidden costs, no ap
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Python Cheatsheet ✅
Python Cheatsheet ✅

𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍 1) Generative AI 2) Big data artificial intelligence 3 ) Microsoft Al f
𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍 1) Generative AI 2) Big data artificial intelligence 3 ) Microsoft Al for beginners 4) Prompt Engineering for Chat GPT 𝐋𝐢𝐧𝐤👇 :-  https://pdlink.in/40Fbg9d Enroll For FREE & Get Certified🎓

7 Baby steps to learn Python: 1. Learn the basics: Start with the fundamentals of Python programming language, such as data types, variables, operators, control structures, and functions. 2. Write simple programs: Start writing simple programs to practice what you have learned. Start with small programs that solve basic problems, such as calculating the factorial of a number, checking whether a number is prime or not, or finding the sum of a sequence of numbers. 3. Work on small projects: Start working on small projects that interest you. These can be simple projects, such as creating a calculator, building a basic game, or automating a task. By working on small projects, you can develop your programming skills and gain confidence. 4. Learn from other people's code: Look at other people's code and try to understand how it works. You can find many open-source projects on platforms like GitHub. Analyze the code, see how it's structured, and try to figure out how the program works. 5. Read Python documentation: Python has extensive documentation, which is very helpful for beginners. Read the documentation to learn more about Python libraries, modules, and functions. 6. Participate in online communities: Participate in online communities like StackOverflow, Reddit, or Python forums. These communities have experienced programmers who can help you with your doubts and questions. 7. Keep practicing: Practice is the key to becoming a good programmer. Keep working on projects, practicing coding problems, and experimenting with different techniques. The more you practice, the better you'll get. Best Resource to learn Python Python Interview Questions with Answers Freecodecamp Python ML Course with FREE Certificate Python for Data Analysis Python course for beginners by Microsoft Scientific Computing with Python Python course by Google Python Free Resources Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍