Coding & AI Resources
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πGet daily updates for : β Free resources β All Free notes β Internship,Jobs and a lot more....π πJoin & Share this channel with your friends and college mates β€οΈ Managed by: @love_data Buy ads: https://telega.io/c/leadcoding
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Network:π Analytical overview of Telegram channel Coding & AI Resources
Channel Coding & AI Resources (@leadcoding) in the English language segment is an active participant. Currently, the community unites 35 264 subscribers, ranking 5 393 in the Education category and 10 892 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 35 264 subscribers.
According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -134 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.41%. Within the first 24 hours after publication, content typically collects 0.75% reactions from the total number of subscribers.
- Post reach: On average, each post receives 850 views. Within the first day, a publication typically gains 266 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as learning, link:-, element, programming, analytic.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βπGet daily updates for :
β
Free resources
β
All Free notes
β
Internship,Jobs
and a lot more....π
πJoin & Share this channel with your friends and college mates β€οΈ
Managed by: @love_data
Buy ads: https://telega.io/c/leadcodingβ
Thanks to the high frequency of updates (latest data received on 01 November, 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.
35 264
Subscribers
-424 hours
-387 days
-13430 days
Posts Archive
35 264
Mastery in programming is not about increasing code complexity. It is about solving increasingly complex problems with simple code.
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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. β₯οΈ
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Complete roadmap to learn data science in 2024 ππ
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
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Machine Learning with Python
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Data Science: Linear Regression Course by Harvard
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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.
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30 days roadmap to learn Python for Data Analysis ππ
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
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