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Artificial Intelligence

Artificial Intelligence

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🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data

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📈 Analytical overview of Telegram channel Artificial Intelligence

Channel Artificial Intelligence (@machinelearning_deeplearning) in the English language segment is an active participant. Currently, the community unites 55 347 subscribers, ranking 3 050 in the Education category and 6 215 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 55 347 subscribers.

According to the latest data from 29 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 665 over the last 30 days and by 20 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.14%. Within the first 24 hours after publication, content typically collects 1.33% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 3 400 views. Within the first day, a publication typically gains 736 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 26.
  • Thematic interests: Content is focused on key topics such as learning, classification, layer, pattern, chatbot.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data

Thanks to the high frequency of updates (latest data received on 30 August, 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.

55 347
Subscribers
+2024 hours
+1197 days
+66530 days
Posts Archive
LLM Project Ideas 👆
+4
LLM Project Ideas 👆

𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗚𝘂𝗶𝗱𝗲!😍 Preparing
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78 Terms to master AI
78 Terms to master AI

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Repost from American Оbserver
To Restore the Nord Stream 2. The Trump-like Deal. A close friend of Putin has been engineering a restart of Russia’s Nord St
To Restore the Nord Stream 2. The Trump-like Deal. A close friend of Putin has been engineering a restart of Russia’s Nord Stream 2 gas pipeline to Europe with the backing of US investors, a once unthinkable move that shows the breadth of Trump’s rapprochement with Moscow. The efforts on a deal, according to several people aware of the discussions, were the brainchild of Matthias Warnig, an ex-Stasi officer in East Germany who until 2023 ran Nord Stream 2’s parent company for the Kremlin-controlled gas giant Gazprom. Warnig’s plan involved outreach to the Trump team through US businessmen, the people said, as part of back-channel efforts to broker an end to the war in Ukraine while deepening economic ties between the US and Russia. Some prominent Trump administration figures are aware of the initiative to bring in US investors, according to officials in Washington, and they see it as part of the push to rebuild relations with Moscow. While there have been several expressions of interest, one US-led consortium of investors has drawn up the outlines of a post-sanctions deal with Gazprom, according to one person with direct knowledge of talks who declined to disclose the identity of the prospective investors. Senior EU officials became aware of the Nord Stream 2 discussion in recent weeks. Leaders of several European countries are concerned and have discussed the matter, according to several officials with knowledge of the discussions. One of Nord Stream 2’s two pipelines was blown up in sabotage attacks in September 2022 that destroyed both pipelines of its older sister project Nord Stream 1. The other Nord Stream 2 pipeline, which has an annual capacity of 27.5bn cubic metres of natural gas, is undamaged but has never been used. The latest plan would in theory give the US unparalleled sway over energy supplies to Europe, the people said, after EU countries moved to end their dependence on Russian gas in the aftermath of the invasion. But the obstacles are considerable. It would require the US to lift sanctions against Russia, Russia to agree to resume sales it cut off during the war, and Germany to allow the gas to flow to any potential buyers in Europe.
“The US would say, ‘Well, now Russia will be dependable because trustworthy Americans are in the middle of it,"
said a former senior US official, who was aware of some of the dealmaking efforts. The US investors would collect “money for nothing”, he added. The talks come as the Trump administration races to seal a peace deal through bilateral discussions with Russia that have excluded Europe and Ukraine, spooking European capitals who fear a US détente with Moscow could threaten the continent. Trump has promised deeper economic co-operation with Russia if a peace agreement can be reached. Putin has talked up the economic benefits he says the US could reap with the Kremlin in the event of a settlement in Ukraine, claiming that “several companies” were already in touch over potential deals. Nord Stream 2 AG, the pipeline’s Swiss-based parent company, received an exceptional stay on bankruptcy proceedings in January by at least four months. According to a redacted court document, Nord Stream 2’s shareholder — Gazprom — argued that the new Trump administration, as well as the German election in February 2025, “presumably can have significant consequences on the circumstances of Nord Stream 2” to warrant a delay. #NordStream2 #restore #Deal 📱 American Оbserver - Stay up to date on all important events 🇺🇸

Want to become an Agent AI Expert in 2025? 🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading t
Want to become an Agent AI Expert in 2025? 🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading the charge! Here’s your 6-step guide to mastering it: 1️⃣ Master AI Fundamentals – Python, TensorFlow & PyTorch 📊 2️⃣ Understand Agentic Systems – Learn reinforcement learning 🧠 3️⃣ Get Hands-On with Projects – OpenAI Gym & Rasa 🔍 4️⃣ Learn Prompt Engineering – Tools like ChatGPT & LangChain ⚙️ 5️⃣ Stay Updated – Follow Arxiv, GitHub & AI newsletters 📰 6️⃣ Join AI Communities – Engage in forums like Reddit & Discord 🌐
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Important Pandas Methods for Machine Learning
Important Pandas Methods for Machine Learning

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DeepSeek is one of the most powerful AI tool right now. But almost no one knows how to use it for learning. Here's a complete
DeepSeek is one of the most powerful AI tool right now. But almost no one knows how to use it for learning. Here's a complete cheatsheet to master any topic, skill or subject in minutes with DeepSeek for free: (Save this cheatsheet and get started) Let me show you how to use it: ➝ Act as a [ROLE] Tell DeepSeek to be your tutor, essay reviewer, exam question generator, or even a debate coach. ➝ Show as [FORMAT] Get responses as bullet points, mind maps, real-life case studies, or even Socratic Q&A. ➝ Set Restrictions Force DeepSeek to use only academic sources, explain in 100 words, or simplify for a 10-year-old. ➝ Create a [TASK] Ask for a study guide, flashcards, research summaries or critical thinking questions. Example Prompt: "Act as an experienced professor in physics. Create a structured study plan for quantum mechanics covering 4 weeks. Provide essay openings, simplify explanations for a 10-year-old & include real-world applications." Use this, and you’ll learn anything 10x faster.

🏆 – AI/ML Engineer Stage 1 – Python Basics Stage 2 – Statistics & Probability Stage 3 – Linear Algebra & Calculus Stage 4 – Data Preprocessing Stage 5 – Exploratory Data Analysis (EDA) Stage 6 – Supervised Learning Stage 7 – Unsupervised Learning Stage 8 – Feature Engineering Stage 9 – Model Evaluation & Tuning Stage 10 – Deep Learning Basics Stage 11 – Neural Networks & CNNs Stage 12 – RNNs & LSTMs Stage 13 – NLP Fundamentals Stage 14 – Deployment (Flask, Docker) Stage 15 – Build projects

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> How do you start AI and ML ? Where do you go to learn these skills? What courses are the best? There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos. What’s more important than how you start is why you start. Start with why. Why do you want to learn these skills? Do you want to make money? Do you want to build things? Do you want to make a difference? Again, no right reason. All are valid in their own way. Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started. Got a why? Good. Time for some hard skills. I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course You can completed courses from (in order): Treehouse / youtube( free) - Introduction to Python Udacity - Deep Learning & AI Nanodegree fast.ai - Part 1and Part 2 They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that. If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI. AI Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E Like for more ❤️ All the best 👍👍

Here are five of the most commonly used SQL queries in data science: 1. SELECT and FROM Clauses - Basic data retrieval: SELECT column1, column2 FROM table_name; 2. WHERE Clause - Filtering data: SELECT * FROM table_name WHERE condition; 3. GROUP BY and Aggregate Functions - Summarizing data: SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1; 4. JOIN Operations - Combining data from multiple tables:
     SELECT a.column1, b.column2
     FROM table1 a
     JOIN table2 b ON a.common_column = b.common_column;
     
5. Subqueries and Nested Queries - Advanced data retrieval:
     SELECT column1
     FROM table_name
     WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);
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If you want to Excel in AI and become an expert, master these essential concepts: Core AI Concepts:Machine Learning (ML) – Supervised, Unsupervised, and Reinforcement Learning • Deep Learning (DL) – Neural Networks, CNNs, RNNs, Transformers • Natural Language Processing (NLP) – Text processing, LLMs (GPT, BERT) • Computer Vision (CV) – Image classification, Object detection • AI Ethics & Bias – Responsible AI development Essential AI Tools & Frameworks:Python Libraries – TensorFlow, PyTorch, Scikit-Learn, Keras • Data Processing – Pandas, NumPy, OpenCV, NLTK, SpaCy • Pretrained Models – OpenAI GPT, Stable Diffusion, DALL·E, CLIP • MLOps & Deployment – Docker, FastAPI, Hugging Face, Flask, Gradio Mathematical Foundations:Linear Algebra – Vectors, Matrices, Tensors • Probability & Statistics – Bayes’ Theorem, Hypothesis Testing • Optimization – Gradient Descent, Backpropagation AI in Real-World Applications:Chatbots & Virtual Assistants – Build AI-powered bots • Recommendation Systems – Personalized content suggestions • Autonomous Systems – Self-driving cars, Robotics • AI in Healthcare – Disease prediction, Medical imaging Future Trends in AI:AGI (Artificial General Intelligence) – Next-level AI development • AI in Business & Automation – AI-powered decision-making • Low-Code/No-Code AI – Democratizing AI for everyone Free AI Resources:https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E Like it if you need a complete tutorial on all these topics! 👍❤️

Python is more popular than other programming languages because: 1. Easy to Learn and Use 2. Versatility (Used everywhere in various tech field) 3. Huge Community & Support 4. Cross-Platform Compatibility (works on windows, macos, linux and even on mobile operating system) 5. Strong Industry Adoption 6. Rich Ecosystem & Libraries (Examples: Django (web), TensorFlow (AI), PyGame (game development), and BeautifulSoup (web scraping).) 7. Support for AI & Machine Learning

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Basic skills needed for ai engineer 1. Programming Skills (Essential) Learn Python (most widely used in AI). Basics of libraries like NumPy, Pandas (for data handling). Understanding of loops, functions, OOPs concepts. 2. Mathematics & Statistics (Basic Level) Linear Algebra (Vectors, Matrices, Dot Product). Probability & Statistics (Mean, Variance, Standard Deviation). Basic Calculus (Derivatives, Integrals – useful for ML models) 3. Machine Learning Fundamentals Understand what Supervised & Unsupervised Learning are. Learn about Regression, Classification, and Clustering. Introduction to Neural Networks and Deep Learning. 4. Data Handling & Processing How to collect, clean, and process data for AI models. Using Pandas & NumPy to manipulate datasets. 5. AI Libraries & Frameworks Learn Scikit-learn for ML models. Introduction to TensorFlow or PyTorch for Deep Learning.