Coding Projects
前往频道在 Telegram
Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data
显示更多📈 Telegram 频道 Coding Projects 的分析概览
频道 Coding Projects (@programming_experts) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 67 472 名订阅者,在 技术与应用 类别中位列第 1 871,并在 印度 地区排名第 4 801 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 67 472 名订阅者。
根据 30 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 449,过去 24 小时变化为 49,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 2.81%。内容发布后 24 小时内通常能获得 1.13% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 1 892 次浏览,首日通常累积 762 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 3。
- 主题关注点: 内容集中在 |--, algorithm, array, framework, javascript 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:
“Channel specialized for advanced concepts and projects to master:
* Python programming
* Web development
* Java programming
* Artificial Intelligence
* Machine Learning
Managed by: @love_data”
凭借高频更新(最新数据采集于 31 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 技术与应用 类别中的关键影响点。
67 472
订阅者
+4924 小时
+1147 天
+44930 天
帖子存档
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🎮💰 भाइयों! आप गेम खेलते हो, लेकिन क्या आप जानते हो उससे पैसे भी कमाए जा सकते हैं? सिर्फ प्लेयर मत बनो, विनर बनो!
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DSA (Data Structures and Algorithms) Essential Topics for Interviews
1️⃣ Arrays and Strings
Basic operations (insert, delete, update)
Two-pointer technique
Sliding window
Prefix sum
Kadane’s algorithm
Subarray problems
2️⃣ Linked List
Singly & Doubly Linked List
Reverse a linked list
Detect loop (Floyd’s Cycle)
Merge two sorted lists
Intersection of linked lists
3️⃣ Stack & Queue
Stack using array or linked list
Queue and Circular Queue
Monotonic Stack/Queue
LRU Cache (LinkedHashMap/Deque)
Infix to Postfix conversion
4️⃣ Hashing
HashMap, HashSet
Frequency counting
Two Sum problem
Group Anagrams
Longest Consecutive Sequence
5️⃣ Recursion & Backtracking
Base cases and recursive calls
Subsets, permutations
N-Queens problem
Sudoku solver
Word search
6️⃣ Trees & Binary Trees
Traversals (Inorder, Preorder, Postorder)
Height and Diameter
Balanced Binary Tree
Lowest Common Ancestor (LCA)
Serialize & Deserialize Tree
7️⃣ Binary Search Trees (BST)
Search, Insert, Delete
Validate BST
Kth smallest/largest element
Convert BST to DLL
8️⃣ Heaps & Priority Queues
Min Heap / Max Heap
Heapify
Top K elements
Merge K sorted lists
Median in a stream
9️⃣ Graphs
Representations (adjacency list/matrix)
DFS, BFS
Cycle detection (directed & undirected)
Topological Sort
Dijkstra’s & Bellman-Ford algorithm
Union-Find (Disjoint Set)
10️⃣ Dynamic Programming (DP)
0/1 Knapsack
Longest Common Subsequence
Matrix Chain Multiplication
DP on subsequences
Memoization vs Tabulation
11️⃣ Greedy Algorithms
Activity selection
Huffman coding
Fractional knapsack
Job scheduling
12️⃣ Tries
Insert and search a word
Word search
Auto-complete feature
13️⃣ Bit Manipulation
XOR, AND, OR basics
Check if power of 2
Single Number problem
Count set bits
Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
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𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍
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If I wanted to get my opportunity to interview at Google or Amazon for SDE roles in the next 6-8 months…
Here’s exactly how I’d approach it (I’ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs):
► Step 1: Learn to Code (from scratch, even if you’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 — pick one)
- FreeCodeCamp on YouTube for beginner-friendly lectures
- Key rule: Don’t just watch. Code along with the video line by line.
Time required: 30–40 days to get good with loops, conditions, syntax.
► Step 2: Start with DSA before jumping to development
Why?
- 90% of tech interviews in top companies focus on Data Structures & Algorithms
- You’ll need time to master it, so start early.
Start with:
- Arrays → Linked List → Stacks → Queues
- You can follow the DSA videos on my channel.
- Practice while learning is a must.
► Step 3: Follow a smart topic order
Once you’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 — note down how you solved each question, what tricks worked, and how you optimized it.
► Step 4: Start giving contests (don’t wait till you’re “ready”)
Most students wait to “finish DSA” before attempting contests.
That’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 — solve the questions you couldn’t during the contest.
► Step 5: Revise smart
Create a “Revision Sheet” with 100 key problems you’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:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
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𝟏𝟎𝟎+ 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
- Data Analytics
- BigData
- Artificial Intelligence
- Cloud Computing
- Data Science
- Machine Learning
- Cyber Security
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𝟰 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 — 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 & 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱!😍
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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 img2pdf67 474
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Learn Django Easily 🤩
Here's all you need to get started 🙌
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 👍👍
#django #webdev
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𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 😍
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Website Development Roadmap – 2025
🔹 Stage 1: HTML – Learn the basics of web page structure.
🔹 Stage 2: CSS – Style and enhance web pages (Flexbox, Grid, Animations).
🔹 Stage 3: JavaScript (ES6+) – Add interactivity and dynamic features.
🔹 Stage 4: Git & GitHub – Manage code versions and collaborate.
🔹 Stage 5: Responsive Design – Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
🔹 Stage 6: UI/UX Basics – Understand user experience and design principles.
🔹 Stage 7: JavaScript Frameworks – Learn React.js, Vue.js, or Angular for interactive UIs.
🔹 Stage 8: Backend Development – Use Node.js, PHP, Python, or Ruby to
build server-side logic.
🔹 Stage 9: Databases – Work with MySQL, PostgreSQL, or MongoDB for data storage.
🔹 Stage 10: RESTful APIs & GraphQL – Create APIs for data communication.
🔹 Stage 11: Authentication & Security – Implement JWT, OAuth, and HTTPS best practices.
🔹 Stage 12: Full Stack Project – Build a fully functional website with both frontend and backend.
🔹 Stage 13: Testing & Debugging – Use Jest, Cypress, or other testing tools.
🔹 Stage 14: Deployment – Host websites using Netlify, Vercel, or cloud services.
🔹 Stage 15: Performance Optimization – Improve website speed (Lazy Loading, CDN, Caching).
📂 Web Development Resources
ENJOY LEARNING 👍👍
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𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍
𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk
𝗝𝗮𝘃𝗮 :- https://pdlink.in/4dWkAMf
𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j
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𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :- https://pdlink.in/4dFem3o
𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw
Get Your Dream Tech Job In Your Dream Company💫
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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)
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🔰 How to become a data scientist in 2025?
👨🏻💻 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.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ 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.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 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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𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗔𝗿𝗲 𝗛𝗶𝗿𝗶𝗻𝗴 𝗡𝗼𝘄😍
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✅ Start applying to jobs that fit your profile and boost your career growth!
