Coding & AI Resources
Ir al canal en Telegram
📚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
Mostrar más
Red:📈 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 35 264 suscriptores, ocupando la posición 5 393 en la categoría Educación y el puesto 10 892 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 35 264 suscriptores.
Según los últimos datos del 31 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -134, y en las últimas 24 horas de -4, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.41%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.75% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 850 visualizaciones. En el primer día suele acumular 266 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- 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 01 noviembre, 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.
35 264
Suscriptores
-424 horas
-387 días
-13430 días
Archivo de publicaciones
35 264
Mastery in programming is not about increasing code complexity. It is about solving increasingly complex problems with simple code.
35 264
Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻
It's easy with Telega.io. As the leading platform for native ads and integrations on Telegram, it provides user-friendly and efficient tools for quick and automated ad launches.
⚡️ Place your ad here in three simple steps:
1 Sign up
2 Top up the balance in a convenient way
3 Create your advertising post
If your ad aligns with our content, we’ll gladly publish it.
Start your promotion journey now!
35 264
The reason you're not feeling motivated is because you don't have a clear goal.
You do have a goal, but it's only that you want to make a lot of money. With just that, you'll only experience FOMO (fear of missing out), not money.
Hard work is your responsibility, but you need to set small and immediate goals. For example, if you're studying DSA, it's not something you can complete in one day. A goal for now should be to master one topic thoroughly until you can solve all medium-level questions, and slowly, you'll crack it.
This is crucial at every stage of life.
Motivation will come when you start achieving small things, and eventually, everything will fall into place one day. ♥️
35 264
Complete roadmap to learn data science in 2024 👇👇
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
35 264
System Design Interview Preparation
System Design Interview Books:
Essential reads for understanding system design concepts and interview questions.
Grokking the System Design Interview by Design Guru:
A practical guide to system design with real-world scenarios.
Designing Data-Intensive Applications:
Learn about the architecture of data systems and how to design data-heavy applications.
35 264
Many people reached out to me saying telegram may get banned in their countries. So I've decided to create WhatsApp channels based on your interests 👇👇
Free Courses with Certificate: https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Jobs & Internship Opportunities:
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Python Free Books & Projects: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Java Resources: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Coding Interviews: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Power BI: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Programming Free Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Data Science Projects: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Learn Data Science & Machine Learning: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Don’t worry Guys your contact number will stay hidden!
ENJOY LEARNING 👍👍
35 264
30 days roadmap to learn Python for Data Analysis 😄👇
Free Resources to Learn Python for Data Analysis: https://t.me/pythonanalyst/102
Days 1-5: Introduction to Python
1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook).
2. Day 2-5: Learn Python basics (variables, data types, and basic operations).
Days 6-10: Control Flow and Functions
6. Day 6-8: Study control flow (if statements, loops).
9. Day 9-10: Learn about functions and modules in Python.
Days 11-15: Data Structures
11. Day 11-12: Explore lists, tuples, and dictionaries.
13. Day 13-15: Study sets and string manipulation.
Days 16-20: Libraries for Data Analysis
16. Day 16-17: Get familiar with NumPy for numerical operations.
18. Day 18-19: Dive into Pandas for data manipulation.
20. Day 20: Basic data visualization with Matplotlib.
Days 21-25: Data Cleaning and Analysis
21. Day 21-22: Data cleaning and preprocessing using Pandas.
23. Day 23-25: Exploratory data analysis (EDA) techniques.
Days 26-30: Advanced Topics
26. Day 26-27: Introduction to data visualization with Seaborn.
27. Day 28-29: Introduction to machine learning with Scikit-Learn.
30. Day 30: Create a small data analysis project.
Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems.
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python Course with FREE Certificate
Python for Data Analysis and Visualization
Python course for beginners by Microsoft
Python course by Google
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING 👍👍
