Python Programming & AI Resources

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✅ Python Programming Books ✅ Coding Projects ✅ Important Pdfs ✅ Artificial Intelligence Courses ✅ Data Science Notes For promotions: @love_data Buy ads: https://telega.io/c/pythonproz

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India31 603 Technologies & Applications9 633...

📈 Analytical overview of Telegram channel Python Programming & AI Resources

Channel Python Programming & AI Resources (@pythonproz) in the English language segment is an active participant. Currently, the community unites 13 224 subscribers, ranking 9 633 in the Technologies & Applications category and 31 603 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 224 subscribers.

According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 17.02%. Within the first 24 hours after publication, content typically collects 5.43% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 251 views. Within the first day, a publication typically gains 718 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
Thematic interests: Content is focused on key topics such as tuple, comprehension, learning, programming, loop.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“✅ Python Programming Books ✅ Coding Projects ✅ Important Pdfs ✅ Artificial Intelligence Courses ✅ Data Science Notes For promotions: @love_data Buy ads: https://telega.io/c/pythonproz”

Thanks to the high frequency of updates (latest data received on 26 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

13 224

Subscribers

+224 hours

+97 days

+7730 days

2 251

Post views

~ 71824 hours

~ 94248 hours

17.02%

Engagement rate

No data

Posts per day

Ads index

beta

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Similar Channels

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Incoming and Outgoing Mentions

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Attracting Subscribers

June '26

+164

in 1 channels

May '26

+98

in 3 channels

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April '26

+58

in 3 channels

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March '26

+90

in 3 channels

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February '26

+241

in 3 channels

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January '26

+441

in 6 channels

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December '25

+127

in 3 channels

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November '25

+480

in 5 channels

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October '25

+1 551

in 4 channels

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September '25

+316

in 1 channels

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August '25

+559

in 5 channels

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July '25

+631

in 4 channels

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June '25

+343

in 3 channels

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May '25

+675

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April '25

+8 635

in 3 channels

Date	Subscriber Growth	Mentions	Channels
26 June	+3
25 June	+4
24 June	+3
23 June	+2
22 June	+6
21 June	+2
20 June	+5
19 June	+2
18 June	+5
17 June	+13
16 June	+28
15 June	+79
14 June	0
13 June	0
12 June	+3
11 June	0
10 June	0
09 June	+2
08 June	+1
07 June	+3
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Channel Posts

🔰 2 Types of Pythonistas

2	IntermediatePython.pdf	2 204
3	𝗣𝗿𝗲𝗶𝗺𝗶𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗚𝘂𝗶𝗱𝗲! 🚀🐍✨ 𝗜𝗻𝗽𝘂𝘁/𝗢𝘂𝘁𝗽𝘂𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📥📤 - print() - input() - format() 𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔄 - int() - float() - str() - bool() - complex() - list() - tuple() - set() - dict() - frozenset() - bytes() - bytearray() - memoryview() 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🧮📐 - abs() - pow() - round() - divmod() - sum() - min() - max() 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 & 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📊📑 - len() - sorted() - range() - zip() - enumerate() - reversed() - all() - any() 𝗧𝘆𝗽𝗲 & 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔍🆔 - type() - id() - isinstance() - issubclass() 𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📂📝 - open() - close() - read() - write() - seek() - tell() 𝗦𝘁𝗿𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔤🔠 - ord() - chr() - ascii() - repr() 𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🛠⚙️ - help() - dir() - eval() - exec() - hash() 𝗟𝗼𝗴𝗶𝗰𝗮𝗹 & 𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🧠🔢 - bin() - oct() - hex() - bool() 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗢𝗯𝗷𝗲𝗰𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 💾📦 - memoryview() - object() - callable() #PythonGuide #PythonFunctions #CodingLife #LearnPython #DevCommunity #PyTips https://t.me/pythonRe ✅	2 396
4	🧩 Local AI is no longer just a toy project. In 2026, you can run a practical AI stack on a laptop: small LLMs, local embeddings, RAG, Jupyter/IDE integration, and no token bill. This post breaks down what is actually usable right now: Qwen, Gemma, Llama, Ollama, Chroma/LanceDB, local RAG, Jupyter AI, hardware limits, and where local video still hurts. Read the local stack	1 388
5	✅ Python Data Types! 🐍✨ Data types define what kind of value a variable stores in Python. name = "Python" age = 25 price = 99.99 is_easy = True 1. String (str): Used to store text values. language = "Python" city = 'Delhi' ✔ Written inside quotes "" or '' ✔ Used for names, messages, text data 2. Integer (int): Used to store whole numbers. age = 25 marks = 95 ✔ No decimal point ✔ Positive or negative numbers allowed 3. Float (float): Used to store decimal numbers. price = 99.99 temperature = 36.6 ✔ Numbers with decimal values 4. Boolean (bool): Used for True or False values. is_logged_in = True is_admin = False ✔ Mostly used in conditions and comparisons 5. List (list): Stores multiple values in one variable. fruits = ["apple", "banana", "mango"] ✔ Ordered collection ✔ Can store duplicate values ✔ Uses square brackets [] 6. Tuple (tuple): Similar to list but cannot be changed. colors = ("red", "blue", "green") ✔ Immutable unchangeable ✔ Uses parentheses () 7. Set (set): Stores unique values only. nums = {1, 2, 3, 3, 4} print(nums) ✔ Output → {1, 2, 3, 4} ✔ Removes duplicates automatically 8. Dictionary (dict): Stores data in key-value pairs. student = { "name": "Alex", "age": 22 } ✔ Uses curly braces {} ✔ Access values using keys 9. Check Data Type: Use type() to check variable type. name = "Python" print(type(name)) ✔ Output → 10. Type Conversion: Convert one data type into another. age = int("25") price = float("99.5") ✔ int() → Integer ✔ float() → Decimal ✔ str() → String 11. Practice Examples: ✔ Add integers a = 10 b = 20 print(a + b) ✔ Print list items fruits = ["apple", "banana"] print(fruits) ✔ Access dictionary value student = {"name": "Alex"} print(student["name"]) 💡 Understanding data types is important because every Python program uses them. 💬 Tap ❤️ if this helped you!	3 340
6	Python Beginner Roadmap 🐍 📂 Start Here ∟📂 Install Python & VS Code ∟📂 Learn How to Run Python Files 📂 Python Basics ∟📂 Variables & Data Types ∟📂 Input & Output ∟📂 Operators (Arithmetic, Comparison) ∟📂 if, else, elif ∟📂 for & while loops 📂 Data Structures ∟📂 Lists ∟📂 Tuples ∟📂 Sets ∟📂 Dictionaries 📂 Functions ∟📂 Defining & Calling Functions ∟📂 Arguments & Return Values 📂 Basic File Handling ∟📂 Read & Write to Files (.txt) 📂 Practice Projects ∟📌 Calculator ∟📌 Number Guessing Game ∟📌 To-Do List (store in file) 📂 ✅ Move to Next Level (Only After Basics) ∟📂 Learn Modules & Libraries ∟📂 Small Real-World Scripts For detailed explanation, join this channel 👇 https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a React "❤️" For More :)	4 073
7	15 Best Project Ideas for Python : 🐍 🚀 Beginner Level: 1. Simple Calculator 2. To-Do List 3. Number Guessing Game 4. Dice Rolling Simulator 5. Word Counter 🌟 Intermediate Level: 6. Weather App 7. URL Shortener 8. Movie Recommender System 9. Chatbot 10. Image Caption Generator 🌌 Advanced Level: 11. Stock Market Analysis 12. Autonomous Drone Control 13. Music Genre Classification 14. Real-Time Object Detection 15. Natural Language Processing (NLP) Sentiment Analysis	3 740
8	Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months ### Week 1: Introduction to Python Day 1-2: Basics of Python - Python setup (installation and IDE setup) - Basic syntax, variables, and data types - Operators and expressions Day 3-4: Control Structures - Conditional statements (if, elif, else) - Loops (for, while) Day 5-6: Functions and Modules - Function definitions, parameters, and return values - Built-in functions and importing modules Day 7: Practice Day - Solve basic problems on platforms like HackerRank or LeetCode ### Week 2: Advanced Python Concepts Day 8-9: Data Structures in Python - Lists, tuples, sets, and dictionaries - List comprehensions and generator expressions Day 10-11: Strings and File I/O - String manipulation and methods - Reading from and writing to files Day 12-13: Object-Oriented Programming (OOP) - Classes and objects - Inheritance, polymorphism, encapsulation Day 14: Practice Day - Solve intermediate problems on coding platforms ### Week 3: Introduction to Data Structures Day 15-16: Arrays and Linked Lists - Understanding arrays and their operations - Singly and doubly linked lists Day 17-18: Stacks and Queues - Implementation and applications of stacks - Implementation and applications of queues Day 19-20: Recursion - Basics of recursion and solving problems using recursion - Recursive vs iterative solutions Day 21: Practice Day - Solve problems related to arrays, linked lists, stacks, and queues ### Week 4: Fundamental Algorithms Day 22-23: Sorting Algorithms - Bubble sort, selection sort, insertion sort - Merge sort and quicksort Day 24-25: Searching Algorithms - Linear search and binary search - Applications and complexity analysis Day 26-27: Hashing - Hash tables and hash functions - Collision resolution techniques Day 28: Practice Day - Solve problems on sorting, searching, and hashing ### Week 5: Advanced Data Structures Day 29-30: Trees - Binary trees, binary search trees (BST) - Tree traversals (in-order, pre-order, post-order) Day 31-32: Heaps and Priority Queues - Understanding heaps (min-heap, max-heap) - Implementing priority queues using heaps Day 33-34: Graphs - Representation of graphs (adjacency matrix, adjacency list) - Depth-first search (DFS) and breadth-first search (BFS) Day 35: Practice Day - Solve problems on trees, heaps, and graphs ### Week 6: Advanced Algorithms Day 36-37: Dynamic Programming - Introduction to dynamic programming - Solving common DP problems (e.g., Fibonacci, knapsack) Day 38-39: Greedy Algorithms - Understanding greedy strategy - Solving problems using greedy algorithms Day 40-41: Graph Algorithms - Dijkstra’s algorithm for shortest path - Kruskal’s and Prim’s algorithms for minimum spanning tree Day 42: Practice Day - Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms ### Week 7: Problem Solving and Optimization Day 43-44: Problem-Solving Techniques - Backtracking, bit manipulation, and combinatorial problems Day 45-46: Practice Competitive Programming - Participate in contests on platforms like Codeforces or CodeChef Day 47-48: Mock Interviews and Coding Challenges - Simulate technical interviews - Focus on time management and optimization Day 49: Review and Revise - Go through notes and previously solved problems - Identify weak areas and work on them ### Week 8: Final Stretch and Project Day 50-52: Build a Project - Use your knowledge to build a substantial project in Python involving DSA concepts Day 53-54: Code Review and Testing - Refactor your project code - Write tests for your project Day 55-56: Final Practice - Solve problems from previous contests or new challenging problems Day 57-58: Documentation and Presentation - Document your project and prepare a presentation or a detailed report Day 59-60: Reflection and Future Plan - Reflect on what you've learned - Plan your next steps (advanced topics, more projects, etc.) Best DSA RESOURCES: https://topmate.io/coding/886874 Credits: https://t.me/free4unow_backup ENJOY LEARNING 👍👍	0
9	Building Chatbots with Python	0
10	Found this - AI Builders, pay attention. A curated marketplace just launched where AI builders list their systems and get paid - setup fee + monthly recurring. No sales, no client chasing. They handle everything, you just build. 100% free to join. No fees, no subscription, no hidden costs. They only take 20% when you earn - on setup fee and recurring. That's it. Accepted builders are earning from day one. Spots are limited by design. Takes 5 minutes to apply. You'll need a 90-second video of your system in action. → https://tglink.io/3508aa8711f389 Daily updates from the CEO: https://tglink.io/e2ba74d53c7039 Follow, like & share in "your network" - these guys are building something seriously worth watching. PS: First systems go live tomorrow. Builders who join early get the best positioning... investor-backed marketing means they bring the clients to you.	0
11	Python Projects for your Data Science Portfolio ⚡️\| Data Analysis Portfolio Projects https://github.com/AlexTheAnalyst/PortfolioProjects ⚡️\| Python for Data Analysis (pydata-book) https://github.com/wesm/pydata-book ⚡️\| Data Science Projects https://github.com/CodeCutTech/Data-science ⚡️\| End-to-End ML Projects https://github.com/GokuMohandas/Made-With-ML ⚡️\| Python Project Scripts https://github.com/hastagAB/Awesome-Python-Scripts ⚡️\| Applied ML in Production https://github.com/eugeneyan/applied-ml ⚡️\| Data Engineering Projects (Zoomcamp) https://github.com/DataTalksClub/data-engineering-zoomcamp ⚡️\| Real-Time Data Processing https://github.com/andkret/Cookbook ⚡️\| Plotly Dash Examples https://github.com/plotly/dash-sample-apps ⚡️\| Streamlit Gallery https://github.com/streamlit/streamlit ⚡️\| Web Scraping Projects https://github.com/NirantK/awesome-project-ideas ⚡️\| API Projects https://github.com/public-apis/public-apis	0
12	+7 🔰 Python Statements	0
13	📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python ▶️ Awesome Python — https://github.com/vinta/awesome-python — the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save ▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python — a huge collection of algorithms and data structures written in Python ▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning — learning Python (and not only) through real projects ▶️ Real Python Guide — https://github.com/realpython/python-guide — a high-quality guide to the Python ecosystem, tools, and best practices ▶️ Materials from Real Python — https://github.com/realpython/materials — a collection of code and projects for Real Python articles and courses ▶️ Learn Python — https://github.com/trekhleb/learn-python — a reference with explanations, examples, and exercises ▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3 — a convenient guide to modern Python 3 with tasks ▶️ Python Reference — https://github.com/rasbt/python_reference — cheat sheets, scripts, and useful tips from one of the most respected Python authors ▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python — a 30-day challenge: from syntax to more complex topics ▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises — 100+ Python tasks with answers ▶️ Coding Problems — https://github.com/MTrajK/coding-problems — tasks on algorithms and data structures, including for preparation for interviews ▶️ Projects — https://github.com/karan/Projects — a list of ideas for pet projects (not just Python). Great for practice ▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code — machine learning in Python in the format of a challenge ▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python — useful snippets and tricks for everyday tasks ▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python — various scripts: from working with the network to automation tasks React ♥️ for more posts like this 💛	0
14	🚀 New Edge for Polymarket Traders: Oracle Lag Sniper A high-performance, open-source strategy repo is making waves right now among serious Polymarket users: the Oracle Lag Sniper. 📈 Why it’s worth your attention: • Exploits oracle timing inefficiencies • Built for fast execution & precise entries • Fully open-source, inspect, modify, and run it yourself 🔗 Check out the repo here: Oracle Lag Sniper GitHub Want more early signals like this, plus private insights and rising strategies to stay ahead of the curve? Subscribe to Polymarket Analytics for exclusive access: Polymarket Analytics Pricing 📊 Don’t just follow the market, get ahead of it.	0

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