Coding Free Books | Python | AI

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Best Channel for Programmers and Hackers All in one channel to learn 👇 1. Python 2. Ethical Hacking 3. Java 4. App development 5. Machine learning 6. Data structures 7. Algorithms Promotions: @coderfun

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网络:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert 印度13 105 教育6 125...

📈 Telegram 频道 Coding Free Books | Python | AI 的分析概览

频道 Coding Free Books | Python | AI (@codingwithsagar) 英语语言赛道中的是活跃参与者。目前社区聚集了 31 165 名订阅者，在教育类别中位列第 6 125，并在印度地区排名第 13 105 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 31 165 名订阅者。

根据 25 八月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 159，过去 24 小时变化为 4，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 2.96%。内容发布后 24 小时内通常能获得 0.88% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 921 次浏览，首日通常累积 274 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 5。
主题关注点： 内容集中在 learning, link:-, css, algorithm, sql 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Best Channel for Programmers and Hackers All in one channel to learn 👇 1. Python 2. Ethical Hacking 3. Java 4. App development 5. Machine learning 6. Data structures 7. Algorithms Promotions: @coderfun”

凭借高频更新（最新数据采集于 26 八月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

31 165

订阅者

+424 小时

+167 天

+15930 天

921

帖子浏览量

~ 27424 小时

~ 36948 小时

2.96%

参与率

无数据

每日帖子数

Ads index

beta

数据加载中...

吸引订阅者

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日期	订阅者增长	提及	频道
26 八月	+3
25 八月	+6
24 八月	+5
23 八月	+2
22 八月	0
21 八月	+4
20 八月	+8
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12 八月	+10
11 八月	+6
10 八月	+24
09 八月	+9
08 八月	+8
07 八月	+5
06 八月	+7
05 八月	+3
04 八月	+14
03 八月	+20
02 八月	+10
01 八月	0

频道帖子

2	Here's a good list of cheat sheets for programmers (all free): Data Science Cheatsheet https://github.com/aaronwangy/Data-Science-Cheatsheet SQL Cheatsheet sqltutorial.org/sql-cheat-sheet t.me/sqlspecialist/827 https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf Java Programming Cheatsheet https://introcs.cs.princeton.edu/java/11cheatsheet/ Javascript Cheatsheet quickref.me/javascript.html t.me/javascript_courses/532 Data Analytics Cheatsheets https://dataanalytics.beehiiv.com/p/data Python Cheat sheet quickref.me/python.html https://t.me/pythondevelopersindia/314 GIT and Machine Learning Cheatsheet https://t.me/datasciencefun/714 HTML Cheatsheet https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf htmlcheatsheet.com CSS Cheatsheet htmlcheatsheet.com/css jQuery Cheatsheet t.me/webdevelopmentbook/90 Data Visualization t.me/datasciencefun/698 ENJOY LEARNING👍👍	1 904
3	Top free Data Science resources 1. CS109 Data Science http://cs109.github.io/2015/pages/videos.html 2. Machine Learning with Python https://www.freecodecamp.org/learn/machine-learning-with-python/ 3. Learning From Data from California Institute of Technology http://work.caltech.edu/telecourse 4. Mathematics for Machine Learning by University of California, Berkeley https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI 5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY 6. Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM 7. CS 221 ― Artificial Intelligence https://stanford.edu/~shervine/teaching/cs-221/ 8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf 9. Python for Data Analysis by Boston University https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx 10. Data Mining bu University of Buffalo https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE Credits: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z	1 758
4	If you’re a student, graduate, or someone looking for a career switch, read this. Most people spend months watching random YouTube videos and still don’t become job-ready. Instead, learn in a structured offline classroom. 📌 Data Analytics with GenAI 📌 Python + SQL + Power BI 📌 6-Month Program 📌 1:1 Mentorship 📌 Job Assistance 📍Now available in your city. Seats are limited. 👉 Register Here: https://lp.pwskills.com/data-analytics-course-offline-batch0?utm_source=telegram&utm_medium=influencer&utm_campaign=daoffline	1 540
5	Quick Recap of Essential Python Concepts 😄👇 Python is a versatile and beginner-friendly programming language widely used in data science, web development, and automation. Here's a quick overview of some fundamental concepts: 1. Variables: * Variables are used to store data values. They are assigned using the = operator. Example: x = 10, name = "Alice" 2. Data Types: * Python has several built-in data types: * Integer (int): Whole numbers (e.g., 1, -5). * Float (float): Decimal numbers (e.g., 3.14, -2.5). * String (str): Textual data (e.g., "Hello", 'Python'). * Boolean (bool): True or False values. * List: Ordered collection of items (e.g., [1, 2, "apple"]). * Tuple: Ordered, immutable collection (e.g., (1, 2, "apple")). * Dictionary: Key-value pairs (e.g., {"name": "Alice", "age": 30}). 3. Operators: * Python supports various operators for performing operations: * Arithmetic Operators: +, -, , /, // (floor division), % (modulus), (exponentiation). * Comparison Operators: ==, !=, >, <, >=, <=. * Logical Operators: and, or, not. * Assignment Operators: =, +=, -=, =, /=, etc. 4. Control Flow: Control flow statements determine the order in which code is executed: * if, elif, else: Conditional execution. * for loop: Iterating over a sequence (list, string, etc.). * while loop: Repeating a block of code as long as a condition is true. 5. Functions: * Functions are reusable blocks of code defined using the def keyword. def greet(name): print("Hello, " + name + "!") greet("Bob") # Output: Hello, Bob! 6. Lists: * Lists are ordered, mutable (changeable) collections. * Create: my_list = [1, 2, 3, "a"] * Access: my_list[0] (first element) * Modify: my_list.append(4), my_list.remove(2) 7. Dictionaries: * Dictionaries store key-value pairs. * Create: my_dict = {"name": "Alice", "age": 30} * Access: my_dict["name"] (gets "Alice") * Modify: my_dict["city"] = "New York" 8. Loops: * For Loops: my_list = [1, 2, 3] for item in my_list: print(item) * While Loops: count = 0 while count < 5: print(count) count += 1 9. String Manipulation: * Slicing: my_string[1:4] (extracts a portion of the string) * Concatenation: "Hello" + " " + "World" * Useful Methods: .upper(), .lower(), .strip(), .replace(), .split() 10. Modules and Libraries: * import statement is used to include code from external modules (libraries). * Example: import math print(math.sqrt(16)) # Output: 4.0 Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Hope it helps :)	1 341
6	Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain? Here's a concise breakdown of each, designed to clarify your options: ### Front-End Development (FE) Key Skills: - HTML/CSS: Fundamental for creating the structure and style of web pages. - JavaScript: Essential for adding interactivity and functionality to websites. - Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development. - Responsive Design: Ensuring websites look good on all devices. - Version Control: Git for managing code changes and collaboration. Career Prospects: - Web Developer - UI/UX Designer - Front-End Engineer ### Back-End Development (BE) Key Skills: - Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic. - Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management. - APIs: RESTful and GraphQL for communication between front-end and back-end. - Server Management: Understanding of server, network, and hosting environments. - Security: Knowledge of authentication, authorization, and data protection. Career Prospects: - Back-End Developer - Full-Stack Developer - Database Administrator ### Machine Learning (ML) Key Skills: - Programming Languages: Python and R are widely used in ML. - Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms. - Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models. - Data Handling: Pandas, NumPy for data manipulation and preprocessing. - Model Evaluation: Techniques for assessing model performance. Career Prospects: - Data Scientist - Machine Learning Engineer - AI Researcher ### Blockchain Key Skills: - Cryptography: Understanding of encryption and security principles. - Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications. - Smart Contracts: Solidity for developing smart contracts. - Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms. - Blockchain Tools: Truffle, Ganache, Metamask for development and testing. Career Prospects: - Blockchain Developer - Smart Contract Developer - Crypto Analyst ### Decision Criteria 1. Interest: Choose an area you are genuinely interested in. 2. Market Demand: Research the current job market to see which skills are in demand. 3. Career Goals: Consider your long-term career aspirations. 4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills. Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives. Here are some telegram channels to help you build your career 👇 Web Development https://t.me/webdevcoursefree Jobs & Internships https://t.me/getjobss Blockchain https://t.me/Bitcoin_Crypto_Web Machine Learning https://t.me/datasciencefun Artificial Intelligence https://t.me/machinelearning_deeplearning Join @free4unow_backup for more free resources. ENJOY LEARNING 👍👍	1 275
7	Aaj hi ek certified Hackar bano!💻 Shuru se saari cheeze seekho bilkul basic se!! PW skills leke aaya h certified Ethical Hacking ka course!! Isme milega : ✅ Hands on Practice ✅ LIVE Hacking Labs ✅ Certificate after Completion Sirf Rs 4999 mai Abhi enroll karo HACK30 Coupon code use karke 30% OFF milega! Enroll NOW : https://pwskills.com/web-development/certified-ethical-hacking-course-035473/?source=pwskills.com&position=course_dropdown&from=home_page&utm_source=pwskills&utm_medium=telegram&utm_campaign=ethical_hacking	1 615
8	5 YouTube Videos That Will Reshape Your Tech Carrier👇 1/ N8N Full Course 6 Hours: https://youtu.be/2GZ2SNXWK-c?si=C1DRnvxBqNBdW5Vp 2/ ChatGPT Tutorial 2025: How to Use ChatGPT - Beginner to Pro!: https://youtu.be/zqVtHYFYQY8?si=ScN5YhJetg37EBIa 3/ How to Build & Sell Al Agents: Ultimate Beginner's Guide: https://youtu.be/w0H1-b044KY?si=J5ko8ovDSmzTvmbG 4/ Improve Your Communication Skills with This! \| John Maxwell: https://youtu.be/S0mbgU239ao?si=X3P-EKMXmCSNAab7 5/ FREE 8 Hour Copywriting Course For Beginners \| $0-$10k/mo In 90 Days: https://youtu.be/OC0nBt3nuDg?si=h5IDlefnHmNB5Iig	2 186
9	+6 🔰 Learn CSS In 20 Days RoadMap	2 466
10	🔰 Web Frameworks in Python	2 412
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13	💻 Software Engineer Roadmap 🚀 📂 Computer Fundamentals ∟📂 Operating Systems (Processes, Threads, Memory, Scheduling) ∟📂 Networking Basics (HTTP/HTTPS, TCP/IP, DNS, APIs) ∟📂 DBMS (SQL, Indexing, Normalization, Transactions) ∟📂 Git & Version Control (GitHub workflow) 📂 Programming Fundamentals ∟📂 Language (Python / JavaScript / Java / C++) ∟📂 Variables, Loops, Functions ∟📂 OOP (Class, Object, Inheritance, Polymorphism) ∟📂 Error Handling & Debugging 📂 Data Structures & Algorithms ∟📂 Arrays, Strings, HashMap ∟📂 Stack, Queue, Linked List ∟📂 Trees, Graphs (Basics) ∟📂 Recursion & Backtracking ∟📂 Patterns (Sliding Window, Two Pointers, Binary Search, DFS/BFS) ∟📂 Dynamic Programming (Basic) 📂 Development (Choose One Path) ∟📂 Web Development 🌐 ∟ Frontend (HTML, CSS, JavaScript, React) ∟ Backend (Node.js / Django / FastAPI) ∟ Database (MongoDB / PostgreSQL) ∟ REST APIs + Authentication ∟📂 Backend / Systems ⚙️ ∟ APIs & Microservices ∟ Databases (SQL + NoSQL) ∟ Caching (Redis) ∟ Message Queues (Kafka/RabbitMQ Basics) ∟📂 AI / Data 🤖 ∟ Python (NumPy, Pandas) ∟ Machine Learning Basics ∟ APIs + AI Integration ∟ LLMs / RAG / AI Apps 📂 Tools & Development Skills ∟📂 Git & GitHub ∟📂 Linux Basics ∟📂 VS Code / IDE ∟📂 Postman (API Testing) ∟📂 Docker (Basics) 📂 System Design (Basics → Advanced) ∟📂 Scalability (Load Balancing, Caching) ∟📂 Database Design ∟📂 API Design ∟📂 Real-world Systems (URL Shortener, Chat App) 📂 Projects (Very Important 🔥) ∟📂 Beginner (Calculator, CLI Apps) ∟📂 Intermediate (CRUD App, Auth System) ∟📂 Advanced (Full Stack App / SaaS / AI Tool) ∟📂 Deploy Projects (Vercel / AWS / Render) 📂 Interview Preparation ∟📂 DSA Practice (LeetCode) ∟📂 Core Subjects Revision (OS, DBMS, CN) ∟📂 Mock Interviews 📂 Portfolio & Resume ∟📂 GitHub Projects ∟📂 Personal Portfolio Website ∟📂 Strong Resume (Project-focused) 📂 Job Preparation ∟📂 Apply Daily (Internships + Jobs) ∟📂 Cold DM + Networking ∟📂 Build Online Presence (LinkedIn / Instagram) ∟✅ Crack Interviews & Become Software Engineer 🚀	2 290
14	🎰 Welcome Bonus 1200% — Maczo Crypto Casino 🎮 Crypto exchange · Sports · Live casino — all in one place 💳 USDT instant deposit & withdrawal →https://tglink.io/b9644e2de9fb21 → Affiliate 60%	1 887
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16	There's a floating-point number in Python and you need to output it as a percentage - use the % format in the f-string x = .023 print(f'{x:.2%}') # 2.30% x = .02375 print(f'{x:.2%}') # 2.38% -- rounded off! x = 1.02375 print(f'{x:.2%}') # 102.38% 👉 @PythonRe	1 990
17	✅ Step-by-Step Approach to Learn Programming 💻🚀 ➊ Pick a Programming Language Start with beginner-friendly languages that are widely used and have lots of resources. ✔ Python – Great for beginners, versatile (web, data, automation) ✔ JavaScript – Perfect for web development ✔ C++ / Java – Ideal if you're targeting DSA or competitive programming Goal: Be comfortable with syntax, writing small programs, and using an IDE. ➋ Learn Basic Programming Concepts Understand the foundational building blocks of coding: ✔ Variables, data types ✔ Input/output ✔ Loops (for, while) ✔ Conditional statements (if/else) ✔ Functions and scope ✔ Error handling Tip: Use visual platforms like W3Schools, freeCodeCamp, or Sololearn. ➌ Understand Data Structures & Algorithms (DSA) ✔ Arrays, Strings ✔ Linked Lists, Stacks, Queues ✔ Hash Maps, Sets ✔ Trees, Graphs ✔ Sorting & Searching ✔ Recursion, Greedy, Backtracking ✔ Dynamic Programming Use GeeksforGeeks, NeetCode, or Striver's DSA Sheet. ➍ Practice Problem Solving Daily ✔ LeetCode (real interview Qs) ✔ HackerRank (step-by-step) ✔ Codeforces / AtCoder (competitive) Goal: Focus on logic, not just solutions. ➎ Build Mini Projects ✔ Calculator ✔ To-do list app ✔ Weather app (using APIs) ✔ Quiz app ✔ Rock-paper-scissors game Projects solidify your concepts. ➏ Learn Git & GitHub ✔ Initialize a repo ✔ Commit & push code ✔ Branch and merge ✔ Host projects on GitHub Must-have for collaboration. ➐ Learn Web Development Basics ✔ HTML – Structure ✔ CSS – Styling ✔ JavaScript – Interactivity Then explore: ✔ React.js ✔ Node.js + Express ✔ MongoDB / MySQL ➑ Choose Your Career Path ✔ Web Dev (Frontend, Backend, Full Stack) ✔ App Dev (Flutter, Android) ✔ Data Science / ML ✔ DevOps / Cloud (AWS, Docker) ➒ Work on Real Projects & Internships ✔ Build a portfolio ✔ Clone real apps (Netflix UI, Amazon clone) ✔ Join hackathons ✔ Freelance or open source ✔ Apply for internships ➓ Stay Updated & Keep Improving ✔ Follow GitHub trends ✔ Dev YouTube channels (Fireship, etc.) ✔ Tech blogs (Dev.to, Medium) ✔ Communities (Discord, Reddit, X) 🎯 Remember: • Consistency > Intensity • Learn by building • Debugging is learning • Track progress weekly Useful WhatsApp Channels to Learn Programming Languages Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L JavaScript: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32 C++ Programming: https://whatsapp.com/channel/0029VbBAimF4dTnJLn3Vkd3M Java Programming: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s 👍 React ♥️ for more	2 067
18	CVE \| Cyber Vulnerabilities Exchange Group dedicated to sharing and discussing CVEs, zero-days, critical vulnerabilities, exploits, PoCs, and technical analyses of offensive and defensive security. What you'll find here: • Newly disclosed CVEs • Public and private exploits • Technical analysis and bypasses • Offensive/defensive security • Penetration testing and red team discussions Technical, direct, and straightforward content. Channel=> https://t.me/cve0day Think. Break. Secure.	0
19	+3 💻 Collection of cheat sheets on SQL I've gathered for you short and understandable cheat sheets on the main topics: ▶️ Basics of the SQL language; ▶️ JOINs with clear examples; ▶️ Window functions; ▶️ SQL for data analysis. An excellent set to refresh your knowledge before a job interview or quickly recall the syntax. tags: #sql #useful https://t.me/DataAnalyticsX	0
20	Master DSA 🚀 DSA MASTER TREE │ ├── 1. Foundations │ ├── What is Data Structure │ ├── What is Algorithm │ ├── Time Complexity │ │ ├── Big-O │ │ ├── Big-Ω │ │ └── Big-Θ │ ├── Space Complexity │ └── Recurrence Relations │ ├── 2. Mathematical Basics │ ├── Logarithms │ ├── Modular Arithmetic │ ├── Prime Numbers │ ├── GCD / LCM │ └── Sieve of Eratosthenes │ ├── 3. Arrays │ ├── Traversal │ ├── Searching │ │ ├── Linear Search │ │ └── Binary Search │ ├── Prefix Sum │ ├── Sliding Window │ ├── Two Pointers │ ├── Kadane’s Algorithm │ └── Matrix / 2D Arrays │ ├── 4. Strings │ ├── String Manipulation │ ├── Pattern Matching │ │ ├── Naive │ │ ├── KMP │ │ ├── Rabin-Karp │ │ └── Z Algorithm │ ├── Palindrome Problems │ ├── String Hashing │ └── Trie │ ├── 5. Linked Lists │ ├── Singly Linked List │ ├── Doubly Linked List │ ├── Circular Linked List │ ├── Reverse Linked List │ ├── Cycle Detection (Floyd) │ └── Merge Lists │ ├── 6. Stack │ ├── Stack Implementation │ ├── Balanced Parentheses │ ├── Next Greater Element │ ├── Monotonic Stack │ └── Min Stack │ ├── 7. Queue │ ├── Queue Implementation │ ├── Circular Queue │ ├── Deque │ ├── Priority Queue │ └── Monotonic Queue │ ├── 8. Hashing │ ├── Hash Tables │ ├── Collision Handling │ │ ├── Chaining │ │ └── Open Addressing │ ├── Load Factor │ └── Rehashing │ ├── 9. Trees │ ├── Binary Tree │ │ ├── Traversals │ │ │ ├── Inorder │ │ │ ├── Preorder │ │ │ └── Postorder │ │ ├── Height / Depth │ │ └── Diameter │ ├── Binary Search Tree │ ├── AVL Tree │ ├── Red-Black Tree │ ├── Segment Tree │ ├── Fenwick Tree │ └── Heap │ ├── Min Heap │ └── Max Heap │ ├── 10. Graphs │ ├── Graph Representation │ │ ├── Adjacency Matrix │ │ └── Adjacency List │ ├── BFS │ ├── DFS │ ├── Topological Sort │ ├── Cycle Detection │ ├── Shortest Path │ │ ├── Dijkstra │ │ ├── Bellman-Ford │ │ └── Floyd-Warshall │ ├── Minimum Spanning Tree │ │ ├── Kruskal │ │ └── Prim │ └── Disjoint Set (Union-Find) │ ├── 11. Recursion & Backtracking │ ├── Recursion Basics │ ├── Subsets │ ├── Permutations │ ├── N-Queens │ └── Sudoku Solver │ ├── 12. Greedy Algorithms │ ├── Activity Selection │ ├── Huffman Coding │ ├── Fractional Knapsack │ └── Job Scheduling │ ├── 13. Dynamic Programming │ ├── Memoization │ ├── Tabulation │ ├── 1D DP │ ├── 2D DP │ ├── Knapsack Variants │ ├── Longest Common Subsequence │ ├── Longest Increasing Subsequence │ └── Matrix Chain Multiplication │ ├── 14. Bit Manipulation │ ├── Bitwise Operators │ ├── Set / Clear Bits │ ├── Count Set Bits │ └── XOR Tricks │ ├── 15. Advanced DSA │ ├── Sparse Table │ ├── Heavy-Light Decomposition │ ├── Treap │ ├── Splay Tree │ └── Skip List │ └── 16. Interview Patterns ├── Two Pointer Pattern ├── Sliding Window Pattern ├── Binary Search Pattern ├── BFS / DFS Pattern ├── Greedy Choice Pattern └── DP Pattern Recognition	0

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