Coding & AI Resources

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📈 Análisis del canal de Telegram Coding & AI Resources

El canal Coding & AI Resources (@leadcoding) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 35 476 suscriptores, ocupando la posición 5 363 en la categoría Educación y el puesto 11 803 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 35 476 suscriptores.

Según los últimos datos del 12 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 74, y en las últimas 24 horas de 1, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.68%. 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 1 307 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 7.
Intereses temáticos: El contenido se centra en temas clave como learning, link:-, element, programming, analytic.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“📚Get daily updates for : ✅ Free resources ✅ All Free notes ✅ Internship,Jobs and a lot more....😍 📍Join & Share this channel with your friends and college mates ❤️ Managed by: @love_data Buy ads: https://telega.io/c/leadcoding”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 13 junio, 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.

35 476

Suscriptores

+124 horas

Sin datos7 días

+7430 días

1 307

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

3.68%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

35 476

Repost from Generative AI

𝟱 𝗕𝗲𝘀𝘁 𝗜𝗕𝗠 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Python for Data Science 2)SQL & Relational Databases 3)Applied Data Science with Python 4)Machine Learning with Python 5)Data Analysis with Python 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/3QyJyqk Enroll For FREE & Get Certified🎓

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Hands-On Data Science and Python Machine Learning Frank Kane, 2017

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𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 Master AI for FREE: 5 Must-Take Google Courses to Boost Your Career 🌟 Artificial Intelligence is transforming industries, and now’s your chance to dive into this exciting field with free, expert-led courses by Google. 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/428e55o Enroll Now & Get Certfied 🎓

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Expert Python Programming (2021) 100 likes = new books

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🌻 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗕𝗶𝗴 𝗢 𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻! O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position. O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half. O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one. O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort. O(n²) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list. O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach. O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.

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𝟱 𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍 Want to gain real-world experience and make your resume stand out? These 100% free & remote virtual internships will help you develop in-demand skills from top global companies! 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4bajU4J Enroll Now & Get Certfied 🎓

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learning-nodejs-development(5).pdf50.54 MB

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𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍 1️⃣ BCG Data Science & Analytics 2️⃣ TATA Data Visualization Internship 3️⃣ Accenture Data Analytics 4️⃣ PwC Power BI Internship 5️⃣ British Airways Data Science 6️⃣ Quantium Data Analytics 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4i9L0LA Enroll For FREE & Get Certified 🎓

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Common Coding Mistakes to Avoid Even experienced programmers make mistakes.

Undefined variables:

Ensure all variables are declared and initialized before use.

Type coercion:

Be mindful of JavaScript's automatic type conversion, which can lead to unexpected results.

Incorrect scope:

Understand the difference between global and local scope to avoid unintended variable access.

Logical errors:

Carefully review your code for logical inconsistencies that might lead to incorrect output.

Off-by-one errors:

Pay attention to array indices and loop conditions to prevent errors in indexing and iteration.

Infinite loops:

Avoid creating loops that never terminate due to incorrect conditions or missing exit points. Example: // Undefined variable error let result = x + 5; // Assuming x is not declared // Type coercion error let age = "30"; let isAdult = age >= 18; // Age will be converted to a number By being aware of these common pitfalls, you can write more robust and error-free code. Do you have any specific coding mistakes you've encountered recently? #javascript #codingtips #errors #bestpractices

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𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 - SQL - Blockchain - HTML & CSS - Excel, and - Generative AI These free full courses will take you from beginner to expert! 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4gRuzlV Enroll For FREE & Get Certified 🎓

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So accurate

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗙𝗥𝗘𝗘 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼𝘀!😍 Want to become a Data Analytics pro?🔥 These tutorials simplify complex topics into easy-to-follow lessons✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4k5x6vx No more excuses—just pure learning!✅️

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🔰 Python From Scratch ☠️ React ❤️ for more free resources 🔗 🔤🔤🔤🔤🔤🔤

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𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 – 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲!😍 Want to break into Machine Learning without spending a fortune?💡 This 100% FREE course is your ultimate guide to learning ML with Python from scratch!✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4k9xb1x 💻 Start Learning Now → Enroll Here✅️

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Get all AI courses, tracks, certifications and projects for FREE this week 🚀 🔗 Registeration link👇 https://datacamp.pxf.io/6ygRrQ Like for more ❤️

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Cheat sheet collection .pdf2.66 MB

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Python Tools to Extract Data.pdf1.06 KB

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Tips on Python List.pdf0.84 KB

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PythonCheatSheet.pdf3.23 KB

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foundations_of_python_network_programming.pdf3.71 MB