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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Сеть:📈 Аналитический обзор Telegram-канала Artificial Intelligence & ChatGPT Prompts
Канал Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 42 275 подписчиков, занимая 3 082 место в категории Технологии и приложения и 8 916 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 42 275 подписчиков.
Согласно последним данным от 15 сентября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -11, а за последние 24 часа — -2, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.71%. В первые 24 часа после публикации контент обычно набирает 0.63% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 724 просмотров. В течение первых суток публикация набирает 266 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 1.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, algorithm, detection, llm, pattern.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“🔓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”
Благодаря высокой частоте обновлений (последние данные получены 16 сентября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
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Посты канала
🧠 AI Concepts Every Beginner Should Know 🤖
🔹 Artificial Intelligence (AI) ➜ Machines performing tasks that normally require human intelligence
🔹 Machine Learning (ML) ➜ Systems that learn patterns from data
🔹 Deep Learning ➜ Uses neural networks with multiple layers to learn complex patterns
🔹 Generative AI ➜ Creates new text, images, audio, video, or code
🔹 Large Language Models (LLMs) ➜ AI models designed to understand and generate human language
🔹 Natural Language Processing (NLP) ➜ Enables computers to process and understand human language
🔹 Computer Vision ➜ Enables machines to understand images and videos
🔹 Neural Networks ➜ Computational models inspired by the way biological neurons process information
🔹 Prompt Engineering ➜ Designing effective instructions for AI models
🔹 RAG ➜ Combines AI models with external knowledge sources to improve responses
🔹 Fine-Tuning ➜ Adapts a pretrained AI model for a specific task or domain
🔹 Embeddings ➜ Represent text or other data as numerical vectors for similarity-based tasks
🔹 Vector Databases ➜ Store and search embeddings efficiently
🔹 AI Agents ➜ AI systems that can reason, use tools, and perform multi-step tasks
🔹 MLOps ➜ Practices for deploying, monitoring, and maintaining machine-learning systems
💡 Double Tap ❤️ For More
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| 2 |  🎓 𝗧𝗼𝗽 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲 🔥
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| 3 |  🚀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝘁𝗼 𝗚𝗲𝘁 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝗶𝗻 𝟮𝟬𝟮𝟲 📊
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| 5 |  🚀 𝗧𝗼𝗽 𝟯 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 🔥
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🎯 Perfect for Students • Freshers • Beginners • Tech Enthusiasts
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| 6 | FREE Resources to Learn Artificial Intelligence 🔥
* Python – python.org/doc
* Math for AI – khanacademy.org/math/statistics‑probability
* Machine Learning – scikit‑learn.org/stable
* Deep Learning – pytorch.org/tutorials
* Generative AI – google.com/ai/learn‑ai‑skills
* NLP (Natural Language Processing) – huggingface.co/learn/nlp‑course
* Reinforcement Learning – openai.com/research
* AI Ethics – resourcelist.ai/ai‑ethics
* AI Research – paperswithcode.com
* AI Projects – kaggle.com/learn/ai
* AI Learning Hub – learn.microsoft.com/en‑us/ai
* GitHub AI List – github.com/mrsaeeddev/free‑ai‑resources
Double Tap ♥️ For More | 860 |
| 7 |  𝗧𝗼𝗽 𝟱 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿 📊
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| 8 |  𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 😍
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⏰ Time: 7:00 PM | 825 |
| 9 |  🚀 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲😍
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| 11 |  🚀 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 📊
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| 14 | ⏩ AI Ethics and Responsible AI:
Understand the challenges associated with AI, including bias, fairness, privacy, transparency, security, hallucinations, copyright, and responsible use of AI systems.
⏩ Build Projects and Practice:
Put your knowledge into practice by building AI projects. Start with simple prediction and classification systems, then progress to chatbots, recommendation systems, computer vision applications, RAG systems, AI agents, and complete AI applications.
⏩ Continuous Learning and AI Trends:
Artificial Intelligence is evolving rapidly. Stay updated with new research, models, tools, frameworks, Generative AI developments, robotics, multimodal AI, and emerging technologies.
➡️ Artificial Intelligence is a vast field that combines programming, mathematics, data, machine learning, deep learning, and intelligent systems. The best way to master AI is to build a strong foundation, practice consistently, and gradually work on real-world projects.
React ❤️ for more
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| 15 | To learn Artificial Intelligence from basic to advanced levels, you can follow these steps: 🤩🤩
⏩ Python Programming:
Start with Python, one of the most popular languages for AI development. Learn variables, data types, functions, loops, object-oriented programming, file handling, and important libraries such as NumPy and Pandas.
⏩ Mathematics for AI:
Build a strong mathematical foundation. Learn linear algebra, probability, statistics, calculus, vectors, matrices, derivatives, gradients, and optimization concepts that form the foundation of modern AI.
⏩ Data Handling and Preprocessing:
Learn how AI systems work with data. Study data collection, cleaning, preprocessing, feature engineering, normalization, encoding, missing values, and handling noisy or unbalanced datasets.
⏩ Machine Learning:
Learn how machines learn patterns from data. Study supervised, unsupervised, and reinforcement learning along with algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Means, and Support Vector Machines.
⏩ Deep Learning:
Move into neural networks and advanced learning systems. Learn neurons, activation functions, forward propagation, backpropagation, loss functions, optimizers, regularization, and architectures such as CNNs, RNNs, LSTMs, and Transformers.
⏩ Natural Language Processing (NLP):
Learn how AI systems understand and generate human language. Study tokenization, text preprocessing, embeddings, sentiment analysis, text classification, sequence models, attention mechanisms, and Transformer architectures.
⏩ Computer Vision:
Teach machines to understand visual information. Learn image processing, image classification, object detection, image segmentation, facial recognition, CNNs, and modern vision models.
⏩ Reinforcement Learning:
Learn how AI agents make decisions through interaction with an environment. Understand agents, states, actions, rewards, policies, value functions, Q-learning, and modern reinforcement-learning techniques.
⏩ Generative AI:
Explore AI systems that can generate new content such as text, images, audio, video, and code. Learn concepts such as generative models, diffusion models, Transformers, and Large Language Models (LLMs).
⏩ Large Language Models (LLMs):
Understand how modern language models work. Study attention, Transformer architecture, pretraining, fine-tuning, instruction tuning, embeddings, context windows, and techniques such as Retrieval-Augmented Generation (RAG).
⏩ AI Agents:
Learn how AI systems can reason through tasks and interact with tools. Explore tool calling, memory, planning, workflows, multi-step task execution, and agent architectures.
⏩ AI Frameworks and Tools:
Become familiar with popular AI development tools and frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, and other modern AI libraries.
⏩ AI Deployment and MLOps:
Learn how to take AI models from experimentation to real-world applications. Study APIs, model serving, Docker, cloud platforms, monitoring, model versioning, data pipelines, and AI system optimization. | 559 |
| 16 |  🚀 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗢𝗻 𝗔𝘇𝘂𝗿𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 ☁️
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🎓 Perfect for Students • Freshers • Data Science Aspirants • AI/ML Learners • Working Professionals | 509 |
| 17 |  𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 — 𝗚𝗲𝘁 𝗣𝗹𝗮𝗰𝗲𝗱 𝗜𝗻 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍
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⚡ Take the first step toward your dream tech career today! | 584 |
| 18 |  🔥 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗙𝗿𝗼𝗺 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱! 💻📊
These free learning resources cover everything from database fundamentals to advanced SQL queries, with opportunities to practice real-world problems.
🎯 Top FREE SQL Resources:
1️⃣ Introduction to Databases & SQL — Udemy
2️⃣ Advanced Database & SQL — Udemy
3️⃣ Learn SQL — Codecademy
4️⃣ SQL Tutorial — SQLZoo
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
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🚀 Start from the basics and work your way toward advanced SQL skills! | 613 |
| 19 |  🚀 𝗗𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗼𝗳 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝘁 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀? 💻🔥
Here’s a collection of company-specific resources to help you understand their interview and hiring processes.
🎯 Interview Preparation Guides For:
🟠 Amazon – Interviewing Guide
🔵 Google – Interview Tips
🪟 Microsoft – Hiring & Interview Tips
🟢 NVIDIA – Hiring Process
🔷 Meta – Software Engineering Interview Prep
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4i6HkgN
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| 20 |  🎓 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀
Learn in-demand skills • Add valuable credentials to your resume
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💻 Infosys :- https://pdlink.in/4eBH3Aa
⚡ IBM :- https://pdlink.in/45KgqDR
💫 Amazon :- https://pdlink.in/47XuBGz
🌐 Cisco :- https://pdlink.in/4gaeVVV
🪟 Microsoft :- https://pdlink.in/4zhGTX6
📢 Save & share this with your friends — start upskilling for FREE! | 742 |
