π Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.iss.one/CodeProgrammer
π Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.iss.one/DataScienceM
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
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π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
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π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
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π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
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π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
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π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
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π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
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π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
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βοΈ Research Papers
Professional Academic Writing & Simulation Services
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Admin: @HusseinSheikho
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.iss.one/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.iss.one/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.iss.one/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.iss.one/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.iss.one/Python53
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY
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Admin: @HusseinSheikho
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π The Problem with AI Browsers: Security Flaws and the End of Privacy
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2025-12-01 | β±οΈ Read time: 5 min read
Current AI-powered browsers, such as Atlas, are facing scrutiny for significant failures in privacy, security, and censorship. These critical flaws raise serious concerns about user data protection and could signal a major threat to digital privacy as the technology becomes more widespread.
#AIBrowsers #Cybersecurity #DataPrivacy #Censorship
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2025-12-01 | β±οΈ Read time: 5 min read
Current AI-powered browsers, such as Atlas, are facing scrutiny for significant failures in privacy, security, and censorship. These critical flaws raise serious concerns about user data protection and could signal a major threat to digital privacy as the technology becomes more widespread.
#AIBrowsers #Cybersecurity #DataPrivacy #Censorship
β€1
π Learning, Hacking, and Shipping ML
π Category: AUTHOR SPOTLIGHTS
π Date: 2025-12-01 | β±οΈ Read time: 11 min read
Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.
#MachineLearning #AI #DataScience #MLOps #Hackathon
π Category: AUTHOR SPOTLIGHTS
π Date: 2025-12-01 | β±οΈ Read time: 11 min read
Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.
#MachineLearning #AI #DataScience #MLOps #Hackathon
β€2
π Why AI Alignment Starts With Better Evaluation
π Category: LARGE LANGUAGE MODELS
π Date: 2025-12-01 | β±οΈ Read time: 16 min read
Achieving true AI alignment is fundamentally dependent on robust evaluation. To ensure AI systems operate according to human values and intentions, we must first develop sophisticated methods to measure their behavior, test for potential risks, and identify misalignments. This goes beyond standard performance benchmarks, requiring a deeper focus on creating comprehensive testing frameworks. Without the ability to accurately assess a model's alignment, any attempt to steer it becomes guesswork, highlighting why better evaluation is the critical first step toward building safer and more reliable AI.
#AIAlignment #AISafety #AIEvaluation #ResponsibleAI
π Category: LARGE LANGUAGE MODELS
π Date: 2025-12-01 | β±οΈ Read time: 16 min read
Achieving true AI alignment is fundamentally dependent on robust evaluation. To ensure AI systems operate according to human values and intentions, we must first develop sophisticated methods to measure their behavior, test for potential risks, and identify misalignments. This goes beyond standard performance benchmarks, requiring a deeper focus on creating comprehensive testing frameworks. Without the ability to accurately assess a model's alignment, any attempt to steer it becomes guesswork, highlighting why better evaluation is the critical first step toward building safer and more reliable AI.
#AIAlignment #AISafety #AIEvaluation #ResponsibleAI
β€2
π How to Generate QR Codes in Python
π Category: PROGRAMMING
π Date: 2025-12-02 | β±οΈ Read time: 7 min read
Unlock the ability to generate QR codes with Python. This beginner-friendly tutorial provides a step-by-step guide to using the popular "qrcode" package. Learn how to easily create and customize QR codes for your applications, from encoding URLs to embedding custom data.
#Python #QRCode #Programming #PythonTutorial
π Category: PROGRAMMING
π Date: 2025-12-02 | β±οΈ Read time: 7 min read
Unlock the ability to generate QR codes with Python. This beginner-friendly tutorial provides a step-by-step guide to using the popular "qrcode" package. Learn how to easily create and customize QR codes for your applications, from encoding URLs to embedding custom data.
#Python #QRCode #Programming #PythonTutorial
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π The Machine Learning Lessons Iβve Learned This Month
π Category: MACHINE LEARNING
π Date: 2025-12-01 | β±οΈ Read time: 4 min read
Discover key machine learning lessons from recent hands-on experience. This monthly review covers the real-world costs and trade-offs of using AI assistants like Copilot, the critical importance of intentionality in project choices (as even a non-choice has consequences), and an exploration of finding unexpected "Christmas connections" within data. A concise look at practical, hard-won insights for ML practitioners.
#MachineLearning #Copilot #AIStrategy #DataScience
π Category: MACHINE LEARNING
π Date: 2025-12-01 | β±οΈ Read time: 4 min read
Discover key machine learning lessons from recent hands-on experience. This monthly review covers the real-world costs and trade-offs of using AI assistants like Copilot, the critical importance of intentionality in project choices (as even a non-choice has consequences), and an exploration of finding unexpected "Christmas connections" within data. A concise look at practical, hard-won insights for ML practitioners.
#MachineLearning #Copilot #AIStrategy #DataScience
β€3
π The Machine Learning βAdvent Calendarβ Day 1: k-NN Regressor in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-01 | β±οΈ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
#MachineLearning #kNN #Excel #DataScience #Regression
π Category: MACHINE LEARNING
π Date: 2025-12-01 | β±οΈ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
#MachineLearning #kNN #Excel #DataScience #Regression
β€3
π The Machine Learning βAdvent Calendarβ Day 2: k-NN Classifier in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-02 | β±οΈ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
#MachineLearning #kNN #Excel #DataScience
π Category: MACHINE LEARNING
π Date: 2025-12-02 | β±οΈ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
#MachineLearning #kNN #Excel #DataScience
β€2
π JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability
π Category: DATA ENGINEERING
π Date: 2025-12-02 | β±οΈ Read time: 12 min read
When processing large JSON payloads, the choice of a parsing library is critical for system performance. This benchmark analysis explores the trade-offs between various libraries, focusing on key metrics like parsing speed, memory consumption, and overall scalability. Discover which tools offer the optimal balance for high-volume data scenarios, helping you make informed decisions for building efficient and resilient applications.
#JSON #Performance #Benchmarking #DataEngineering #Backend
π Category: DATA ENGINEERING
π Date: 2025-12-02 | β±οΈ Read time: 12 min read
When processing large JSON payloads, the choice of a parsing library is critical for system performance. This benchmark analysis explores the trade-offs between various libraries, focusing on key metrics like parsing speed, memory consumption, and overall scalability. Discover which tools offer the optimal balance for high-volume data scenarios, helping you make informed decisions for building efficient and resilient applications.
#JSON #Performance #Benchmarking #DataEngineering #Backend
β€3
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Found a great resource for everyone who wants to improve their math skills for deep learning β this section
No fluff, just what you need to work in ML. Mathematical analysis, linear algebra, probability theory β all in a convenient format and immediately with code
A nice bonus: you can choose the dialect in which examples are shown (PyTorch, Keras, or MXNET).
https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html
By the way, the other chapters are just as worthyπΎ
π± @DataScienceM
No fluff, just what you need to work in ML. Mathematical analysis, linear algebra, probability theory β all in a convenient format and immediately with code
A nice bonus: you can choose the dialect in which examples are shown (PyTorch, Keras, or MXNET).
https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html
By the way, the other chapters are just as worthy
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π The Architecture Behind Web Search in AI Chatbots
π Category: LLM APPLICATIONS
π Date: 2025-12-04 | β±οΈ Read time: 16 min read
Explore the technical architecture powering web search in AI chatbots. This analysis breaks down how generative models retrieve and integrate live web data to provide current answers, highlighting the crucial shift towards Generative Engine Optimization (GEO). Learn what this new paradigm means for content visibility in an AI-first search landscape, moving beyond traditional SEO.
#AI #GEO #Chatbots #Search #RAG
π Category: LLM APPLICATIONS
π Date: 2025-12-04 | β±οΈ Read time: 16 min read
Explore the technical architecture powering web search in AI chatbots. This analysis breaks down how generative models retrieve and integrate live web data to provide current answers, highlighting the crucial shift towards Generative Engine Optimization (GEO). Learn what this new paradigm means for content visibility in an AI-first search landscape, moving beyond traditional SEO.
#AI #GEO #Chatbots #Search #RAG
β€2
π Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch
π Category: DEEP LEARNING
π Date: 2025-12-03 | β±οΈ Read time: 10 min read
Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.
#PyTorch #PerformanceOptimization #DeepLearning #MLOps
π Category: DEEP LEARNING
π Date: 2025-12-03 | β±οΈ Read time: 10 min read
Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.
#PyTorch #PerformanceOptimization #DeepLearning #MLOps
β€4
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π The Machine Learning βAdvent Calendarβ Day 3: GNB, LDA and QDA in Excel
π Category: MACHINE LEARNING
π Date: 2025-12-03 | β±οΈ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
#MachineLearning #Excel #DataScience #LDA #Statistics
π Category: MACHINE LEARNING
π Date: 2025-12-03 | β±οΈ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
#MachineLearning #Excel #DataScience #LDA #Statistics
β€4
π How to Turn Your LLM Prototype into a Production-Ready System
π Category: LLM APPLICATIONS
π Date: 2025-12-03 | β±οΈ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
π Category: LLM APPLICATIONS
π Date: 2025-12-03 | β±οΈ Read time: 15 min read
Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.
#LLM #MLOps #ProductionAI #LLMOps
β€3
π Multi-Agent Arena: Insights from London Great Agent Hack 2025
π Category: AGENTIC AI
π Date: 2025-12-03 | β±οΈ Read time: 16 min read
Key insights from the London Great Agent Hack 2025 reveal critical success factors for multi-agent systems. The focus has shifted towards building highly robust agents capable of withstanding adversarial testing and unexpected scenarios. A major theme was the importance of "glass-box reasoning"βmaking agent decision-making transparent and interpretable. Ultimately, red-team resilience and explainability, not just raw performance, were the defining characteristics of the top-performing solutions.
#MultiAgentSystems #AIAgents #ExplainableAI #AISecurity
π Category: AGENTIC AI
π Date: 2025-12-03 | β±οΈ Read time: 16 min read
Key insights from the London Great Agent Hack 2025 reveal critical success factors for multi-agent systems. The focus has shifted towards building highly robust agents capable of withstanding adversarial testing and unexpected scenarios. A major theme was the importance of "glass-box reasoning"βmaking agent decision-making transparent and interpretable. Ultimately, red-team resilience and explainability, not just raw performance, were the defining characteristics of the top-performing solutions.
#MultiAgentSystems #AIAgents #ExplainableAI #AISecurity
π How to Code Your Own Website with AI
π Category: AGENTIC AI
π Date: 2025-12-03 | β±οΈ Read time: 8 min read
Unlock the potential of AI in web development. This guide explains how to "vibe-code" a website, a modern method where AI tools translate your high-level design concepts and desired feel directly into functional code. Learn a more intuitive and streamlined approach to building websites from scratch.
#AI #WebDevelopment #AICoding #DeveloperTools #GenerativeAI
π Category: AGENTIC AI
π Date: 2025-12-03 | β±οΈ Read time: 8 min read
Unlock the potential of AI in web development. This guide explains how to "vibe-code" a website, a modern method where AI tools translate your high-level design concepts and desired feel directly into functional code. Learn a more intuitive and streamlined approach to building websites from scratch.
#AI #WebDevelopment #AICoding #DeveloperTools #GenerativeAI
β€3
π How to Use Simple Data Contracts in Python for Data Scientists
π Category: DATA SCIENCE
π Date: 2025-12-02 | β±οΈ Read time: 5 min read
Prevent your data pipelines from breaking unexpectedly. This article demonstrates how to implement simple data contracts in Python using Pandera, an open-source validation library. Learn to define and enforce data quality rules to build more robust and reliable data science workflows, ensuring your data meets expectations before it causes issues downstream.
#DataContracts #Python #DataScience #Pandera #DataValidation
π Category: DATA SCIENCE
π Date: 2025-12-02 | β±οΈ Read time: 5 min read
Prevent your data pipelines from breaking unexpectedly. This article demonstrates how to implement simple data contracts in Python using Pandera, an open-source validation library. Learn to define and enforce data quality rules to build more robust and reliable data science workflows, ensuring your data meets expectations before it causes issues downstream.
#DataContracts #Python #DataScience #Pandera #DataValidation
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This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
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