Data Science & Machine Learning Resources

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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free Admin: @love_data Buy ads: https://telega.io/c/datalemur

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India20 748 Educación9 739...

📈 Análisis del canal de Telegram Data Science & Machine Learning Resources

El canal Data Science & Machine Learning Resources (@datalemur) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 20 460 suscriptores, ocupando la posición 9 739 en la categoría Educación y el puesto 20 748 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 20 460 suscriptores.

Según los últimos datos del 02 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 42, y en las últimas 24 horas de -2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.70%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 0 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
Intereses temáticos: El contenido se centra en temas clave como |--, learning, insidead, database, sql.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free Admin: @love_data Buy ads: https://telega.io/c/datalemur”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 03 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

20 460

Suscriptores

-224 horas

-117 días

+4230 días

756

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

3.70%

Tasa de compromiso

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Archivo de publicaciones

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🎯Free AI/ML learning resources 👇 1/ Google Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course 2/ Kaggle Learn: https://www.kaggle.com/learn 3/ Harvard CS50 AI with Python: https://cs50.harvard.edu/ai/ 4/ DeepLearning.AI Short Courses: https://www.deeplearning.ai/courses/ 5/ TensorFlow Official Tutorials: https://www.tensorflow.org/tutorials

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Data Science Interview Questions.pdf1.42 MB

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SQL vs Python Programming: Quick Comparison ✍ 📌 SQL Programming • Query data from databases • Filter, join, aggregate rows Best fields • Data Analytics • Business Intelligence • Reporting and MIS • Entry-level Data Engineering Job titles • Data Analyst • Business Analyst • BI Analyst • SQL Developer Hiring reality • Asked in most analyst interviews • Used daily in analyst roles India salary range • Fresher: 4–8 LPA • Mid-level: 8–15 LPA Real tasks • Monthly sales report • Top customers by revenue • Duplicate removal 📌 Python Programming • Clean and analyze data • Automate workflows • Build models Where you work • Notebooks • Scripts • ML pipelines Best fields • Data Science • Machine Learning • Automation • Advanced Analytics Job titles • Data Scientist • ML Engineer • Analytics Engineer • Python Developer Hiring reality • Common in mid to senior roles • Strong demand in AI teams India salary range • Fresher: 6–10 LPA • Mid-level: 12–25 LPA Real tasks • Churn prediction • Report automation • File handling CSV, Excel, JSON ⚔️ Quick comparison • Data source SQL stays inside databases Python pulls data from anywhere • Speed SQL runs fast on large tables Python slows with raw big data • Learning SQL is beginner-friendly Python needs coding basics 🎯 Role-based choice • Data Analyst SQL required Python adds value • Data Scientist Python required SQL used to fetch data • Business Analyst SQL works for most roles Python helps automate work • Data Engineer SQL for pipelines Python for processing ✅ Best career move • Learn SQL first for entry • Add Python for growth • Use both in real projects Which one do you prefer? SQL 👍 Python ❤️ Both 🙏 None 😮

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📊 Data Science Roadmap 🚀 📂 Start Here ∟📂 What is Data Science & Why It Matters? ∟📂 Roles (Data Analyst, Data Scientist, ML Engineer) ∟📂 Setting Up Environment (Python, Jupyter Notebook) 📂 Python for Data Science ∟📂 Python Basics (Variables, Loops, Functions) ∟📂 NumPy for Numerical Computing ∟📂 Pandas for Data Analysis 📂 Data Cleaning & Preparation ∟📂 Handling Missing Values ∟📂 Data Transformation ∟📂 Feature Engineering 📂 Exploratory Data Analysis (EDA) ∟📂 Descriptive Statistics ∟📂 Data Visualization (Matplotlib, Seaborn) ∟📂 Finding Patterns & Insights 📂 Statistics & Probability ∟📂 Mean, Median, Mode, Variance ∟📂 Probability Basics ∟📂 Hypothesis Testing 📂 Machine Learning Basics ∟📂 Supervised Learning (Regression, Classification) ∟📂 Unsupervised Learning (Clustering) ∟📂 Model Evaluation (Accuracy, Precision, Recall) 📂 Machine Learning Algorithms ∟📂 Linear Regression ∟📂 Decision Trees & Random Forest ∟📂 K-Means Clustering 📂 Model Building & Deployment ∟📂 Train-Test Split ∟📂 Cross Validation ∟📂 Deploy Models (Flask / FastAPI) 📂 Big Data & Tools ∟📂 SQL for Data Handling ∟📂 Introduction to Big Data (Hadoop, Spark) ∟📂 Version Control (Git & GitHub) 📂 Practice Projects ∟📌 House Price Prediction ∟📌 Customer Segmentation ∟📌 Sales Forecasting Model 📂 ✅ Move to Next Level ∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch) ∟📂 NLP (Text Analysis, Chatbots) ∟📂 MLOps & Model Optimization Data Science Resources: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z React "❤️" for more! 🚀📊

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Resonant is a mini-app that connects your decision patterns to your AI Agents. Generate your personal Agentic Memory Card now! https://t.me/ResonantAlphaBot/resonant?startapp

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ChatGPT Prompts Book Oliver Theobald, 2024

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Most people who have valuable knowledge never turn it into a course Not because they can’t — but because it feels too complicated Content, structure, platforms, tech... I came across something interesting: LUMILY - AI tool that turns your idea into a full course and launches it straight in Telegram No LMS No tech overhead No complicated setup Just your expertise → structured lessons Feels like a shortcut that shouldn’t exist 👉 Try Live Demo

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🤖 𝗔𝗜 + 𝗗𝗮𝘁𝗮 = 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗝𝗼𝗯𝘀 Start your journey in Data Analytics & Data Science with AI Certification and gain skills companies are actively hiring for. 📊 Data Analysis 🐍 Python Programming 🤖 Machine Learning 📈 AI-Driven Insights 🔥 Perfect for College Students ,Freshers & Professionals 1️⃣𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3OD9jI1 2️⃣𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 :- https://pdlink.in/4kucM7E 3️⃣𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4ay4wPG 4️⃣𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/3ZtIZm9 5️⃣𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 :- https://pdlink.in/4rMivIA Don't Miss This Opportunity . Get Placement Assistance With 5000+ Companies

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DSA Roadmap For AI/Ml Roadmap 🚀 Double Tap ♥️ For More

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🚀 𝐒𝐛𝐞𝐫𝟓𝟎𝟎 𝐁𝐚𝐭𝐜𝐡 𝟕 — 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 𝐟𝐨𝐫 𝐃𝐞𝐞𝐩𝐓𝐞𝐜𝐡 & 𝐀𝐈 𝐒𝐭𝐚𝐫𝐭𝐮𝐩𝐬 👨🏻‍💻 If you're building science-intensive technology in AI, robotics, or advanced computing — this is for you. 🔹 𝐖𝐡𝐨 𝐒𝐡𝐨𝐮𝐥𝐝 𝐀𝐩𝐩𝐥𝐲 ✅ Startups with MVP and early traction ✅ DeepTech teams working on: • GenAI & Applied AI for Scientific Research • Robotics & Autonomous Transport Systems • Advanced Materials & Photonics • Quantum Computing • Earth Remote Sensing (Space & Ground-based) ✅ International founders exploring the Russian market 🔹 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 1️⃣ Stage 1: Online bootcamp for 150 teams → Strengthen product strategy & business model → Identify market use cases for your technology → Assess collaboration opportunities with Sber ecosystem 2️⃣ Stage 2: 25 best teams selected → Work with international mentors (serial founders, VC partners, corporate executives) → Access to actively investing funds → Direct discussions with potential corporate customers 3️⃣ Stage 3: Demo Day at Moscow Startup Summit (Fall 2026) → Present to wider audience → In 2024 & 2025, every 5th startup at Demo Day was international 🔹 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐆𝐞𝐭 ✅ 12-week online program in English ✅ International mentors from Europe, US, Asia, Middle East ✅ Access to investors & corporations ✅ Long-term community (work continues after program ends) 🔹 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐭𝐡𝐚𝐭 𝐒𝐩𝐞𝐚𝐤 📈 Revenue grows 4x on average after program 🚀 Some teams scale up to 1,000x 🤝 10,900+ contracts and pilots with corporations (6 seasons) 🔹 𝐏𝐫𝐞𝐯𝐢𝐨𝐮𝐬 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐓𝐞𝐚𝐦𝐬 𝐅𝐫𝐨𝐦: India, South Korea, Armenia, China, Turkey, Algeria and other countries 📅 𝐃𝐞𝐚𝐝𝐥𝐢𝐧𝐞: 10 𝐀𝐩𝐫𝐢𝐥 𝟐𝟎𝟐𝟔 💰 𝐏𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐢𝐨𝐧: 𝐅𝐫𝐞𝐞 𝐨𝐟 𝐂𝐡𝐚𝐫𝐠𝐞 👉 𝐀𝐩𝐩𝐥𝐲 𝐍𝐨𝐰 #DataScience #MachineLearning #DeepTech #GenAI #Startup #Accelerator #AI #VentureCapital ENJOY 👍👍

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Math For Machine Learning 🚀 React ❤️ For More

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End to End ML Project

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🧠 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍)⁣ 🔹 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐨𝐝𝐚𝐲⁣ 𝐖𝐡𝐚𝐭 𝐊𝐍𝐍 𝐢𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬⁣ 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐊𝐍𝐍 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧⁣ 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐊 (𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫)⁣ 𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: 𝐄𝐮𝐜𝐥𝐢𝐝𝐞𝐚𝐧 𝐯𝐬 𝐌𝐚𝐧𝐡𝐚𝐭𝐭𝐚𝐧⁣ 𝐖𝐡𝐲 𝐊𝐍𝐍 𝐢𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐚 𝐥𝐚𝐳𝐲 / 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐞𝐫⁣ ⁣ 🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣ ⁣ 1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘒-𝘕𝘦𝘢𝘳𝘦𝘴𝘵 𝘕𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘴 (𝘒𝘕𝘕)?⁣ 2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘒𝘕𝘕 𝘤𝘢𝘭𝘭𝘦𝘥 𝘢 𝘭𝘢𝘻𝘺 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮?⁣ 3️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘒𝘕𝘕 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘒𝘕𝘕 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣ 4️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘒?⁣ 5️⃣ 𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘸𝘩𝘦𝘯 𝘒 𝘪𝘴 𝘵𝘰𝘰 𝘴𝘮𝘢𝘭𝘭 𝘰𝘳 𝘵𝘰𝘰 𝘭𝘢𝘳𝘨𝘦?⁣ 6️⃣ 𝘞𝘩𝘢𝘵 𝘥𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘒𝘕𝘕?⁣ 7️⃣ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮 𝘱𝘰𝘰𝘳𝘭𝘺 𝘰𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢?⁣ 8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘰𝘧 𝘒𝘕𝘕?⁣ 9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘒𝘋-𝘛𝘳𝘦𝘦 𝘢𝘯𝘥 𝘉𝘢𝘭𝘭-𝘛𝘳𝘦𝘦 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦?⁣ 🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 #𝘒𝘕𝘕?⁣

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🔗 Complete Machine Learning Handwritten Notes 📝

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Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape 🔘Pro is currently the #1 open-source model worldwide 🔘Lite (2B parameters) outperforms Sora v1. 🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21. Useful links 🔘Full leaderboard: LM Arena 🔘Kandinsky 5.0 details: technical report 🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

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Machine Learning Handwritten Notes.pdf16.99 MB

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Machine Learning Fundamentals A structured Machine Learning Fundamentals guide covering core concepts, intuition, math basics, ML algorithms, deep learning, and real-world workflows. https://t.me/CodeProgrammer 🎀

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📊 A comprehensive summary of the «Seaborn Library» 👨🏻‍💻 One of the best choices for any data scientist to convert data into clear and beautiful charts, so that they can better understand what the data is saying and also be able to present the results correctly and clearly to others, is the Seaborn library. ✅ A very user-friendly library for creating professional charts with minimal coding. It is built on top of Matplotlib but is simpler and easier to use than that. ✏️ With this summary, you will learn the syntax, see many examples and real applications of #Seaborn, and ultimately help you elevate your #datavisualization skills by several levels. 🌐 #Data_Science #DataScience https://t.me/DataAnalyticsX 🌟 React 💖 for more amazing content

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𝗜𝗳 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗶𝗻 𝘁𝗼𝘀𝘀𝗲𝘀… Think again! 🎲 Here’s why it’s a game-changer for anyone in data science, analytics, and decision-making: ➜ Decode Uncertainty From weather forecasts to financial markets, probability helps us make smarter choices. ➜ Master Essential Distributions Understand Binomial, Poisson, Normal, and more in the simplest way possible. ➜ Crack Data Science Interviews #Probability is a key topic in analytics and #machinelearning interviews. ➜ Avoid Common Misconceptions Learn why "50-50 odds" don’t always mean a fair game. ➜ Visualize Concepts, Not Just Formulas The best way to learn is through intuitive graphs and real-world examples!