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Coding Projects

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Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data

Network: Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertIndia4 646Technologies & Applications1 912...

📈 Analytical overview of Telegram channel Coding Projects

Channel Coding Projects (@programming_experts) in the English language segment is an active participant. Currently, the community unites 67 947 subscribers, ranking 1 912 in the Technologies & Applications category and 4 646 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 947 subscribers.

According to the latest data from 06 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 21 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 3.37%. Within the first 24 hours after publication, content typically collects 1.01% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 291 views. Within the first day, a publication typically gains 686 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
Thematic interests: Content is focused on key topics such as |--, algorithm, array, framework, javascript.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data”

Thanks to the high frequency of updates (latest data received on 07 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 Technologies & Applications category.

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67 947

Subscribers

-1024 hours

+327 days

+2130 days

2 291

Post views

~ 68624 hours

~ 94348 hours

3.37%

Engagement rate

~ 1

Posts per day

Ads index

Posts Archive

67 947

Practical Cryptography in Python Seth James Nielson, 2019

67 947

Bubble Shooter Game.zip7.77 MB

67 947

Here is the bubble shooter game in Python

67 947

Python Machine Learning 👇 book

67 947

Spring 5 Design Patterns Author: Dinesh Rajput

67 947

Practical Network Automation - Second Edition

67 947

Practical Network Automation - Second Edition Network automation is the use of IT controls to supervise and carry out everyday network management functions. It plays a key role in network virtualization technologies and network functions. The book starts by providing an introduction to network automation, and its applications, which include integrating DevOps tools to automate the network efficiently. It then guides you through different network automation tasks and covers various data digging and performing tasks such as ensuring golden state configurations using templates, interface parsing. This book also focuses on Intelligent Operations using Artificial Intelligence and troubleshooting using chatbots and voice commands. The book then moves on to the use of Python and the management of SSH keys for machine-to-machine (M2M) communication, all followed by practical use cases. The book also covers the importance of Ansible for network automation, including best practices in automation; ways to test automated networks using tools such as Puppet, SaltStack, and Chef; and other important techniques. Through practical use-cases and examples, this book will acquaint you with the various aspects of network automation. It will give you the solid foundation you need to automate your own network without any hassle.

67 947

Angular 2+ Notes for Professionals book 🔗 Download this book

67 947

Artificial Intelligence for a Better Future by Bernd Carsten Stahl 📄 128 pages 🔗 Book link:

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Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales. Let's talk about it 🧵 There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are: ▪️ Normalization ▪️ Standardization The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses. Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1. This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value. In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance). More about them: ▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms. ▪️Standardization is robust to outliers. ▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2. Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works. https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data. https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key. Enable gradient descent to converge faster

67 947

Managing Machine Learning Projects Simon Thompson, 2022

67 947

Raspberry Pi IoT Projects John C. Shovic, 2021

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Curated papers, articles, and blogs on data science & machine learning in production. https://github.com/eugeneyan/applied-ml

67 947

✅100+ Free Courses for you all😍 👇👇 1. Python Programming 2. Java Programming 3. Web Development 4. Data Science 5. Artificial Intelligence 6. Machine learning 7. C++ Programming 8. And many more Don’t miss it 💯 Register here 👇 https://www.edyoda.com/register?aff=9 Use Invite Code: EDYODA ENJOY LEARNING 👍👍

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Algorithms-Leetcode-Javascript Webpack questions/answers you can use to prepare for interviews or test your knowledge. Creator: Stepan V Stars ⭐️: 178 Forked By : 60 GitHub Repo: https://github.com/styopdev/webpack-interview-questions

67 947

Algorithms-Leetcode-Javascript Creator: ignacio-chiazzo Stars ⭐️: 297 Forked By : 38 GitHub Repo: https://github.com/ignacio-chiazzo/Algorithms-Leetcode-Javascript #javascript

67 947

Join us for free python resources, ebooks, latest projects and source codes. Learn and dicuss with best of the pythonic minds. https://t.me/pyGuru

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Building Chatbots with Python - 2019 👊Join now👊 👉@Python3learning