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 Expert India5 113 Technologies & Applications1 981...

📈 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 66 163 subscribers, ranking 1 981 in the Technologies & Applications category and 5 113 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 66 163 subscribers.

According to the latest data from 19 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 784 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 3.20%. Within the first 24 hours after publication, content typically collects 0.95% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 117 views. Within the first day, a publication typically gains 627 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
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 20 June, 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.

66 163

Subscribers

+124 hours

+1397 days

+78430 days

2 117

Post views

~ 62724 hours

~ 82648 hours

3.20%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

66 166

Mastering MongoDB 6.x Alex Giamas, 2022

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Machine Learning Project 5 - Income Classification using ML.zip6.21 KB

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Machine_Learning_Project_84_Sentiment_Analysis_Dow_Jones_DJIA_Stock.zip3.53 MB

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Machine_Learning_Project_4_Indian_classical_dance_problem_using.zip1.75 MB

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Machine Learning Project 3 - Boston Housing Analysis.zip0.27 KB

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Machine Learning Project 2 - Bitcoin Price Prediction.zip0.18 KB

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Machine Learning Projects 👇

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Practical Cryptography in Python Seth James Nielson, 2019

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Bubble Shooter Game.zip7.77 MB

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Here is the bubble shooter game in Python

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Python Machine Learning 👇 book

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Spring 5 Design Patterns Author: Dinesh Rajput

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Practical Network Automation - Second Edition

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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.

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Angular 2+ Notes for Professionals book 🔗 Download this book

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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

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Managing Machine Learning Projects Simon Thompson, 2022

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Raspberry Pi IoT Projects John C. Shovic, 2021