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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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India12 820 Education6 240...

📈 Analytical overview of Telegram channel Coding Free Books | Python | AI

Channel Coding Free Books | Python | AI (@codingwithsagar) in the English language segment is an active participant. Currently, the community unites 31 095 subscribers, ranking 6 240 in the Education category and 12 820 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 31 095 subscribers.

According to the latest data from 26 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -82 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 5.89%. Within the first 24 hours after publication, content typically collects 0.84% reactions from the total number of subscribers.
Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 262 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
Thematic interests: Content is focused on key topics such as learning, link:-, css, algorithm, sql.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 27 October, 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 Education category.

31 095

Subscribers

-224 hours

-397 days

-8230 days

1 831

Post views

~ 26224 hours

~ 32248 hours

5.89%

Engagement rate

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Posts per day

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✅ Node.js Basics You Should Know 🌐 Node.js lets you run JavaScript on the server side, making it great for building fast, scalable backend applications. 🚀 1️⃣ What is Node.js? Node.js is a runtime built on Chrome's V8 JavaScript engine. It enables running JS outside the browser, mainly for backend development. 🖥️ 2️⃣ Why Use Node.js? - Fast & non-blocking (asynchronous) ⚡ - Huge npm ecosystem 📦 - Same language for frontend & backend 🔄 - Ideal for APIs, real-time apps, microservices 💬 3️⃣ Core Concepts: - Modules: Reusable code blocks (e.g., fs, http, custom modules) 🧩 - Event Loop: Handles async operations ⏳ - Callbacks & Promises: For non-blocking code 🤝 4️⃣ Basic Server Example:

const http = require('http');

http.createServer((req, res) => {
  res.writeHead(200, {'Content-Type': 'text/plain'});
  res.end('Hello, Node.js!');
}).listen(3000); // Server listening on port 3000

5️⃣ npm (Node Package Manager): Install libraries like Express, Axios, etc.

npm init
npm install express

6️⃣ Express.js (Popular Framework):

const express = require('express');
const app = express();

app.get('/', (req, res) => res.send('Hello World!'));
app.listen(3000, () => console.log('Server running on port 3000'));

7️⃣ Working with JSON & APIs:

app.use(express.json()); // Middleware to parse JSON body
app.post('/data', (req, res) => {
  console.log(req.body); // Access JSON data from request body
  res.send('Received!');
});

8️⃣ File System Module (fs):

const fs = require('fs');
fs.readFile('file.txt', 'utf8', (err, data) => {
  if (err) throw err;
  console.log(data); // Content of file.txt
});

9️⃣ Middleware in Express: Functions that run before reaching the route handler.

app.use((req, res, next) => {
  console.log('Request received at:', new Date());
  next(); // Pass control to the next middleware/route handler
});

🔟 Real-World Use Cases: - REST APIs 📊 - Real-time apps (chat, notifications) 💬 - Microservices 🏗️ - Backend for web/mobile apps 📱 💡 Tip: Once you're confident, explore MongoDB, JWT auth, and deployment with platforms like Vercel or Render. 💬 Tap ❤️ for more!

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✅ AI Foundations – Learn the Core Concepts First 🧠📘 Mastering AI starts with strong fundamentals. Here’s what to focus on: 1️⃣ Math Basics You’ll need these for understanding models, optimization, and predictions: ⦁ Linear Algebra: Vectors, matrices, dot products, eigenvalues ⦁ Calculus: Derivatives, gradients for backpropagation in neural nets ⦁ Probability & Statistics: Distributions, Bayes theorem, standard deviation, hypothesis testing 2️⃣ Python Programming Python is the primary language for AI development. Learn: ⦁ Data types, loops, functions ⦁ List comprehensions, OOP basics ⦁ Practice with small scripts and problem sets 3️⃣ Data Structures & Algorithms Important for writing efficient code: ⦁ Arrays, stacks, queues, trees ⦁ Searching and sorting ⦁ Time and space complexity 4️⃣ Data Handling Skills AI models rely on clean, structured data: ⦁ NumPy: Numerical arrays and matrix operations ⦁ Pandas: DataFrames, filtering, grouping ⦁ Matplotlib/Seaborn: Data visualization 5️⃣ Basic Machine Learning Concepts Before deep learning, understand: ⦁ What is supervised/unsupervised learning? ⦁ Feature engineering ⦁ Bias-variance tradeoff ⦁ Cross-validation 6️⃣ Tools Setup Start with: ⦁ Jupyter Notebook or Google Colab ⦁ Anaconda for local package management ⦁ Use version control with Git & GitHub 7️⃣ First Projects to Try ⦁ Linear regression on salary data ⦁ Classifying flowers with Iris dataset ⦁ Visualizing Titanic survival with Pandas and Seaborn 📌 Build your foundation step by step. No shortcuts. Double Tap ❤️ For More

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10 projects that will get you shortlisted at MNCs 1. Airline Reservation System: - https://lnkd.in/dRFK2vPh 2. Data Visualization Software: - https://lnkd.in/dVWVU8xn 3. Electricity Billing System: - https://lnkd.in/dekycNXQ 4. E-Healthcare Management System: - https://lnkd.in/dEkE2raN 5. Email Client Software: - https://lnkd.in/d_qz7U9E 6. Library Management System: - https://lnkd.in/dY7bDjFn 7. Network Packet Sniffer: - https://lnkd.in/dXPtyzz4 8. RSS Feed Reader: - https://lnkd.in/dupDQPnG 9. Smart City Project: - https://lnkd.in/d3YT36aJ 10. Virtual Private Network: - https://lnkd.in/dyEcgrFC Tips to create these projects: 1. Don’t copy-paste it online; add your touch. 2. Create at least 5-6 unique projects and include them in your resume. 3. Ensure you have proper documentation for each project. Double Tap ❤️ For More

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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👍👍

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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

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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

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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 :)

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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 👍👍

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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

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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

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🔰 Web Frameworks in Python

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💻 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 🚀

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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