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Artificial Intelligence & ChatGPT Prompts

Artificial Intelligence & ChatGPT Prompts

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🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

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📈 Telegram kanali Artificial Intelligence & ChatGPT Prompts analitikasi

Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 42 261 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 082-o'rinni va Hindiston mintaqasida 9 009-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 42 261 obunachiga ega bo‘ldi.

28 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 43 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 1.50% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.68% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 632 marta ko‘riladi; birinchi sutkada odatda 289 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent learning, algorithm, detection, llm, pattern kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

Yuqori yangilanish chastotasi (oxirgi ma’lumot 29 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

42 261
Obunachilar
-224 soatlar
-437 kunlar
+4330 kunlar
Postlar arxiv
𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐝 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂'𝐬 😍 Learn Coding From Scratch - Lectures Taug
𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐝 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂'𝐬 😍 Learn Coding From Scratch - Lectures Taught By IIT Alumni 60+ Hiring Drives Every Month 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:-  🌟 Trusted by 7500+ Students 🤝 500+ Hiring Partners 💼 Avg. Rs. 7.4 LPA 🚀 41 LPA Highest Package Eligibility: BTech / BCA / BSc / MCA / MSc 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-  https://pdlink.in/4hO7rWY Hurry, limited seats available!🏃‍♀️

Breaking into Machine Learning doesn’t need to be complicated. If you’re just starting out, Here’s how to simplify your approach: Avoid: 🚫 Trying to master every algorithm and framework (XGBoost, CNNs, GANs, etc.) from day one.  🚫 Spending too much time on heavy math before touching a dataset.  🚫 Copy-pasting code without understanding what's happening.  🚫 Thinking you need to build the next ChatGPT to be relevant. Instead: ✅ Start with the basics of Python and libraries like NumPy, Pandas, and Matplotlib.  ✅ Understand key concepts like supervised vs. unsupervised learning and basic algorithms (like Linear Regression, KNN, Decision Trees).  ✅ Pick simple, clean datasets (like from Kaggle or UCI) and apply what you learn.  ✅ Focus on explaining your process—what’s the problem, how you approached it, and what you found.  ✅ Build a portfolio of practical ML projects with clear storytelling and insights. React ♥️ for more

𝗜𝗜𝗧 & 𝗜𝗜𝗠 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍 👉Open for all. No Coding Background Required
𝗜𝗜𝗧 & 𝗜𝗜𝗠 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍 👉Open for all. No Coding Background Required AI/ML By IIT Patna  :- https://pdlink.in/41ZttiU Business Analytics With AI :- https://pdlink.in/41h8gRt Digital Marketing With AI :-https://pdlink.in/47BxVYG AI/ML By IIT Mandi :- https://pdlink.in/4cvXBaz 🔥Get Placement Assistance With 5000+ Companies🎓

Complete Roadmap to Become a Data Scientist 📂 1. Learn the Basics of Programming – Start with Python (preferred) or R – Focus on variables, loops, functions, and libraries like numpy, pandas 📂 2. Math & Statistics – Probability, Statistics, Mean/Median/Mode – Linear Algebra, Matrices, Vectors – Calculus basics (for ML optimization) 📂 3. Data Handling & Analysis – Data cleaning (missing values, outliers) – Data wrangling with pandas – Exploratory Data Analysis (EDA) with matplotlib, seaborn 📂 4. SQL for Data – Querying data, joins, aggregations – Subqueries, window functions – Practice with real datasets 📂 5. Machine Learning – Supervised: Linear Regression, Logistic Regression, Decision Trees – Unsupervised: Clustering, PCA – Tools: scikit-learn, xgboost, lightgbm 📂 6. Deep Learning (Optional Advanced) – Basics of Neural Networks – Frameworks: TensorFlow, Keras, PyTorch – CNNs, RNNs for image/text tasks 📂 7. Projects & Real Datasets – Kaggle Competitions – Build projects like Movie Recommender, Stock Prediction, or Customer Segmentation 📂 8. Data Visualization & Dashboarding – Tools: matplotlib, seaborn, Plotly, Power BI, Tableau – Create interactive reports 📂 9. Git & Deployment – Version control with Git – Deploy ML models with Flask or Streamlit 📂 10. Resume + Portfolio – Host projects on GitHub – Share insights on LinkedIn – Apply for roles like Data Analyst → Jr. Data Scientist → Data Scientist Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D 👍 Tap ❤️ for more!

𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜😍 Curriculum designed and taught by
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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 👍👍

𝗔𝗜/𝗠𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗕𝘆 𝗩𝗶𝘀𝗵𝗹𝗲𝘀𝗮𝗻 𝗶-𝗛𝘂𝗯, 𝗜𝗜𝗧 𝗣𝗮𝘁𝗻𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁
𝗔𝗜/𝗠𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗕𝘆  𝗩𝗶𝘀𝗵𝗹𝗲𝘀𝗮𝗻 𝗶-𝗛𝘂𝗯, 𝗜𝗜𝗧 𝗣𝗮𝘁𝗻𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻😍 Freshers are getting paid 10 - 15 Lakhs by learning AI & ML skill Upgrade your career with a beginner-friendly AI/ML certification. 👉Open for all. No Coding Background Required 💻 Learn AI/ML from Scratch 🎓 Build real world Projects for job ready portfolio  🔥Deadline :- 19th April     𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :-  https://pdlink.in/41ZttiU . Get Placement Assistance With 5000+ Companies

Don't Confuse to learn Python. Learn This Concept to be proficient in Python. 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻: - Python Syntax - Data Types - Variables - Operators - Control Structures: if-elif-else Loops Break and Continue try-except block - Functions - Modules and Packages 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻: - Classes and Objects - Inheritance - Polymorphism - Encapsulation - Abstraction 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀: - Pandas - Numpy 𝗣𝗮𝗻𝗱𝗮𝘀: - What is Pandas? - Installing Pandas - Importing Pandas - Pandas Data Structures (Series, DataFrame, Index) 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: - Creating DataFrames - Accessing Data in DataFrames - Filtering and Selecting Data - Adding and Removing Columns - Merging and Joining DataFrames - Grouping and Aggregating Data - Pivot Tables 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻: - Handling Missing Values - Handling Duplicates - Data Formatting - Data Transformation - Data Normalization 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀: - Handling Large Datasets with Dask - Handling Categorical Data with Pandas - Handling Text Data with Pandas - Using Pandas with Scikit-learn - Performance Optimization with Pandas 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻: - Lists - Tuples - Dictionaries - Sets 𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻: - Reading and Writing Text Files - Reading and Writing Binary Files - Working with CSV Files - Working with JSON Files 𝗡𝘂𝗺𝗽𝘆: - What is NumPy? - Installing NumPy - Importing NumPy - NumPy Arrays 𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: - Creating Arrays - Accessing Array Elements - Slicing and Indexing - Reshaping Arrays - Combining Arrays - Splitting Arrays - Arithmetic Operations - Broadcasting 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆: - Reading and Writing Data with NumPy - Filtering and Sorting Data - Data Manipulation with NumPy - Interpolation - Fourier Transforms - Window Functions 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆: - Vectorization - Memory Management - Multithreading and Multiprocessing - Parallel Computing I have curated the best resources to learn Python 👇👇 https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Hope you'll like it Like this post if you need more resources like this 👍❤️ #Python

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If you're serious about learning Artificial Intelligence (AI) — follow this roadmap 🤖🧠 1. Learn Python basics (variables, loops, functions, OOP) 🐍 2. Master NumPy Pandas for data handling 📊 3. Learn data visualization tools: Matplotlib, Seaborn 📈 4. Study math essentials: linear algebra, probability, stats ➗ 5. Understand machine learning fundamentals: – Supervised vs unsupervised – Train/test split, cross-validation – Overfitting, underfitting, bias-variance 6. Learn scikit-learn: regression, classification, clustering 🧮 7. Work on real datasets (Titanic, Iris, Housing, MNIST) 📂 8. Explore deep learning: neural networks, activation, backpropagation 🧠 9. Use TensorFlow or PyTorch for model building ⚙️ 10. Build basic AI models (image classifier, sentiment analysis) 🖼️📜 11. Learn NLP concepts: tokenization, embeddings, transformers ✍️ 12. Study LLMs: how GPT, BERT, and LLaMA work 📚 13. Build AI mini-projects: chatbot, recommender, object detection 🤖 14. Learn about Generative AI: GANs, diffusion, image generation 🎨 15. Explore tools like Hugging Face, OpenAI API, LangChain 🧩 16. Understand ethical AI: fairness, bias, privacy 🛡️ 17. Study AI use cases in healthcare, finance, education, robotics 🏥💰🤖 18. Learn model evaluation: accuracy, F1, ROC, confusion matrix 📏 19. Learn model deployment: FastAPI, Flask, Streamlit, Docker 🚀 20. Document everything on GitHub + create a portfolio site 🌐 21. Follow AI research papers/blogs (arXiv, PapersWithCode) 📄 22. Add 1–2 strong AI projects to your resume 💼 23. Apply for internships or freelance gigs to gain experience 🎯 Tip: Pick small problems and solve them end-to-end—data to deployment. 💬 Tap ❤️ for more!

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