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Coding Projects

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Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data

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Coding Projects (@programming_experts) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 66 046 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 982-o'rinni va Hindiston mintaqasida 5 209-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 66 046 obunachiga ega boโ€˜ldi.

12 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 749 ga, soโ€˜nggi 24 soatda esa 34 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 3.78% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.29% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 2 495 marta koโ€˜riladi; birinchi sutkada odatda 853 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 8 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent |--, algorithm, array, framework, javascript kabi asosiy mavzularga jamlangan.

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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œChannel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_dataโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 13 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

66 046
Obunachilar
+3424 soatlar
+1357 kunlar
+74930 kunlar
Postlar arxiv
๐Ÿ”… Barcode Generation using Python โญ๏ธ https://t.me/LearnPython3
๐Ÿ”… Barcode Generation using Python โญ๏ธ https://t.me/LearnPython3

How to convert image to pdf in Python # Python3 program to convert image to pfd # using img2pdf library   # importing necessary libraries import img2pdf from PIL import Image import os   # storing image path img_path = "Input.png"   # storing pdf path pdf_path = "file_pdf.pdf"   # opening image image = Image.open(img_path)   # converting into chunks using img2pdf pdf_bytes = img2pdf.convert(image.filename)   # opening or creating pdf file file = open(pdf_path, "wb")   # writing pdf files with chunks file.write(pdf_bytes)   # closing image file image.close()   # closing pdf file file.close()   # output print("Successfully made pdf file") pip3 install pillow && pip3 install img2pdf

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Learn Django Easily ๐Ÿคฉ Here's all you need to get started ๐Ÿ™Œ 1. Introduction to Django    - What is Django?    - Setting up the Development Environment 2. Django Basics    - Django Project Structure    - Apps in Django    - Settings and Configuration 3. Models    - Creating Models    - Migrations    - Model Relationships 4. Views    - Function-Based Views    - Class-Based Views    - Generic Views 5. Templates    - Template Syntax    - Template Inheritance    - Template Tags and Filters 6. Forms    - Creating Forms    - Form Validation    - Model Forms 7. URLs and Routing    - URLconf    - Named URL Patterns    - URL Namespaces 8. Django ORM    - Querying the Database    - QuerySets    - Aggregations 9. Authentication and Authorization    - User Authentication    - Permission and Groups    - Django's Built-in User Model 10. Static Files and Media     - Serving Static Files     - File Uploads     - Managing Media Files 11. Middleware     - Using Middleware     - Creating Custom Middleware 12. REST Framework     - Django REST Framework (DRF)     - Serializers     - ViewSets and Routers 13. Testing     - Writing Tests     - Testing Models, Views, and Forms     - Test Coverage 14. Internationalization and Localization     - Translating Strings     - Time Zones 15. Security     - Securing Django Applications     - CSRF Protection     - XSS Protection 16. Deployment     - Deploying with WSGI and ASGI     - Using Gunicorn     - Deploying to Heroku, AWS, etc. 17. Optimization     - Database Optimization     - Caching Strategies     - Profiling and Performance Monitoring 18. Best Practices     - Code Structure     - DRY Principle     - Reusable Apps Web Development Best Resources: https://topmate.io/coding/930165 ENJOY LEARNING ๐Ÿ‘๐Ÿ‘ #django #webdev

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Website Development Roadmap โ€“ 2025 ๐Ÿ”น Stage 1: HTML โ€“ Learn the basics of web page structure. ๐Ÿ”น Stage 2: CSS โ€“ Style and enhance web pages (Flexbox, Grid, Animations). ๐Ÿ”น Stage 3: JavaScript (ES6+) โ€“ Add interactivity and dynamic features. ๐Ÿ”น Stage 4: Git & GitHub โ€“ Manage code versions and collaborate. ๐Ÿ”น Stage 5: Responsive Design โ€“ Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS). ๐Ÿ”น Stage 6: UI/UX Basics โ€“ Understand user experience and design principles. ๐Ÿ”น Stage 7: JavaScript Frameworks โ€“ Learn React.js, Vue.js, or Angular for interactive UIs. ๐Ÿ”น Stage 8: Backend Development โ€“ Use Node.js, PHP, Python, or Ruby to build server-side logic. ๐Ÿ”น Stage 9: Databases โ€“ Work with MySQL, PostgreSQL, or MongoDB for data storage. ๐Ÿ”น Stage 10: RESTful APIs & GraphQL โ€“ Create APIs for data communication. ๐Ÿ”น Stage 11: Authentication & Security โ€“ Implement JWT, OAuth, and HTTPS best practices. ๐Ÿ”น Stage 12: Full Stack Project โ€“ Build a fully functional website with both frontend and backend. ๐Ÿ”น Stage 13: Testing & Debugging โ€“ Use Jest, Cypress, or other testing tools. ๐Ÿ”น Stage 14: Deployment โ€“ Host websites using Netlify, Vercel, or cloud services. ๐Ÿ”น Stage 15: Performance Optimization โ€“ Improve website speed (Lazy Loading, CDN, Caching). ๐Ÿ“‚ Web Development Resources ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills: 1. Analysis of Sales Data: (https://www.kaggle.com/kyanyoga/sample-sales-data) 2. HR Analytics: (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) 3. Social Media Analytics: (https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels) 4. Financial Data Analysis: (https://www.kaggle.com/datasets/nitindatta/finance-data) 5. Healthcare Data Analysis: (https://www.kaggle.com/cdc/mortality) 6. Customer Relationship Management: (https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data) 7. Web Analytics: (https://www.kaggle.com/zynicide/wine-reviews) 8. E-commerce Analysis: (https://www.kaggle.com/olistbr/brazilian-ecommerce) 9. Supply Chain Management: (https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis) 10. Inventory Management: (https://www.kaggle.com/datasets?search=inventory+management) Share this channel with your friends ๐Ÿค๐Ÿคฉ Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐Ÿ”ฐ How to become a data scientist in 2025? ๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field. ๐Ÿ”ข Step 1: Strengthen your math and statistics! โœ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master: โœ… Linear algebra: matrices, vectors, eigenvalues. ๐Ÿ”— Course: MIT 18.06 Linear Algebra โœ… Calculus: derivative, integral, optimization. ๐Ÿ”— Course: MIT Single Variable Calculus โœ… Statistics and probability: Bayes' theorem, hypothesis testing. ๐Ÿ”— Course: Statistics 110 โž–โž–โž–โž–โž– ๐Ÿ”ข Step 2: Learn to code. โœ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are: โœ… Python: Pandas, NumPy, Matplotlib libraries ๐Ÿ”— Course: FreeCodeCamp Python Course โœ… SQL language: Join commands, Window functions, query optimization. ๐Ÿ”— Course: Stanford SQL Course โœ… Data structures and algorithms: arrays, linked lists, trees. ๐Ÿ”— Course: MIT Introduction to Algorithms โž–โž–โž–โž–โž– ๐Ÿ”ข Step 3: Clean and visualize data โœ๏ธ Learn how to process and clean data and then create an engaging story from it! โœ… Data cleaning: Working with missing values โ€‹โ€‹and detecting outliers. ๐Ÿ”— Course: Data Cleaning โœ… Data visualization: Matplotlib, Seaborn, Tableau ๐Ÿ”— Course: Data Visualization Tutorial โž–โž–โž–โž–โž– ๐Ÿ”ข Step 4: Learn Machine Learning โœ๏ธ It's time to enter the exciting world of machine learning! You should know these topics: โœ… Supervised learning: regression, classification. โœ… Unsupervised learning: clustering, PCA, anomaly detection. โœ… Deep learning: neural networks, CNN, RNN ๐Ÿ”— Course: CS229: Machine Learning โž–โž–โž–โž–โž– ๐Ÿ”ข Step 5: Working with Big Data and Cloud Technologies โœ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing. โœ… Big Data Tools: Hadoop, Spark, Dask โœ… Cloud platforms: AWS, GCP, Azure ๐Ÿ”— Course: Data Engineering โž–โž–โž–โž–โž– ๐Ÿ”ข Step 6: Do real projects! โœ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio. โœ… Kaggle competitions: solving real-world challenges. โœ… End-to-End projects: data collection, modeling, implementation. โœ… GitHub: Publish your projects on GitHub. ๐Ÿ”— Platform: Kaggle๐Ÿ”— Platform: ods.ai โž–โž–โž–โž–โž– ๐Ÿ”ข Step 7: Learn MLOps and deploy models โœ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model. โœ… MLOps training: model versioning, monitoring, model retraining. โœ… Deployment models: Flask, FastAPI, Docker ๐Ÿ”— Course: Stanford MLOps Course โž–โž–โž–โž–โž– ๐Ÿ”ข Step 8: Stay up to date and network โœ๏ธ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field. โœ… Read scientific articles: arXiv, Google Scholar โœ… Connect with the data community: ๐Ÿ”— Site: Papers with code ๐Ÿ”— Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data

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DSA Handwritten Notes
+8
DSA Handwritten Notes

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๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—๐—ฎ๐˜ƒ๐—ฎ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ: ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜โ€™๐˜€ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ-๐—™๐—ฟ๐—ถ๐—ฒ๐—ป๐—ฑ๐—น๐˜† ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ ๐˜๐—ผ ๐—ž๐—ถ๐—ฐ๐—ธ๐˜€๐˜๐—ฎ๐—ฟ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—–๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ๐Ÿ˜ ๐Ÿ‘จโ€๐Ÿ’ป Want to learn Java from scratch โ€” without spending a rupee?๐Ÿ’ฐ Youโ€™re in luck! Microsoft has launched a free, beginner-friendly Java course designed to help anyone, from complete newbies to curious career-switchers, start coding with confidence๐Ÿ‘จโ€๐ŸŽ“๐Ÿ“Œ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/43L195Q This course is your perfect starting point๐Ÿ˜

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Beginnerโ€™s Roadmap to Learn Data Structures & Algorithms 1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation. 2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently. 3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation. 4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data. 5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving. 6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation. 7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems. 8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding. 9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges. 10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio. Best DSA RESOURCES: https://topmate.io/coding/886874 All the best ๐Ÿ‘๐Ÿ‘

๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฒ๐—บ๐—ผ ๐—–๐—น๐—ฎ๐˜€๐˜€ ๐—œ๐—ป ๐—›๐˜†๐—ฑ๐—ฒ๐—ฟ๐—ฎ๐—ฏ๐—ฎ๐—ฑ ๐Ÿ˜ ๐Ÿ“Š โ€œData Analystโ€ is one of the hottest c
๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฒ๐—บ๐—ผ ๐—–๐—น๐—ฎ๐˜€๐˜€ ๐—œ๐—ป ๐—›๐˜†๐—ฑ๐—ฒ๐—ฟ๐—ฎ๐—ฏ๐—ฎ๐—ฑ ๐Ÿ˜ ๐Ÿ“Š โ€œData Analystโ€ is one of the hottest careers in tech โ€” and guess what? NO coding needed!  Now itโ€™s YOUR turn to break into tech! ๐Ÿ’ผ Hereโ€™s what you get:- โœ…No Coding Required โœ…100% Placement Support โœ…Offline Classes in Hyderabad with Expert Mentors  โœ…Real-world Projects & Industry Certification  ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฒ๐—ฟ ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ‘‡:- https://pdlink.in/4kFhjn3 Location:- Gachibowli Centre, Hyderabad!

If I wanted to get my opportunity to interview at Google or Amazon for SDE roles in the next 6-8 monthsโ€ฆ Hereโ€™s exactly how Iโ€™d approach it (Iโ€™ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs): โ–บ Step 1: Learn to Code (from scratch, even if youโ€™re from non-CS background) I helped my sister go from zero coding knowledge (she studied Biology and Electrical Engineering) to landing a job at Microsoft. We started with: - A simple programming language (C++, Java, Python โ€” pick one) - FreeCodeCamp on YouTube for beginner-friendly lectures - Key rule: Donโ€™t just watch. Code along with the video line by line. Time required: 30โ€“40 days to get good with loops, conditions, syntax. โ–บ Step 2: Start with DSA before jumping to development Why? - 90% of tech interviews in top companies focus on Data Structures & Algorithms - Youโ€™ll need time to master it, so start early. Start with: - Arrays โ†’ Linked List โ†’ Stacks โ†’ Queues - You can follow the DSA videos on my channel. - Practice while learning is a must. โ–บ Step 3: Follow a smart topic order Once youโ€™re done with basics, follow this path: 1. Searching & Sorting 2. Recursion & Backtracking 3. Greedy 4. Sliding Window & Two Pointers 5. Trees & Graphs 6. Dynamic Programming 7. Tries, Heaps, and Union Find Make revision notes as you go โ€” note down how you solved each question, what tricks worked, and how you optimized it. โ–บ Step 4: Start giving contests (donโ€™t wait till youโ€™re โ€œreadyโ€) Most students wait to โ€œfinish DSAโ€ before attempting contests. Thatโ€™s a huge mistake. Contests teach you: - Time management under pressure - Handling edge cases - Thinking fast Platforms: LeetCode Weekly/ Biweekly, Codeforces, AtCoder, etc. And after every contest, do upsolving โ€” solve the questions you couldnโ€™t during the contest. โ–บ Step 5: Revise smart Create a โ€œRevision Sheetโ€ with 100 key problems youโ€™ve solved and want to reattempt. Every 2-3 weeks, pick problems randomly and solve again without seeing solutions. This trains your recall + improves your clarity. Coding Projects:๐Ÿ‘‡ https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502 ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐€๐ฆ๐š๐ณ๐จ๐ง ๐…๐‘๐„๐„ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ ๐Ÿ˜ Learn AI for free with Amazon's incredible courses! These
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Scientific programming in python cheat sheet
Scientific programming in python cheat sheet

๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜ From data science and AI to web development and cloud c
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Seaborn Cheatsheet โœ…
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Seaborn Cheatsheet โœ