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

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India5 113 Tecnologías y Aplicaciones1 981...

📈 Análisis del canal de Telegram Coding Projects

El canal Coding Projects (@programming_experts) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 66 163 suscriptores, ocupando la posición 1 981 en la categoría Tecnologías y Aplicaciones y el puesto 5 113 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 66 163 suscriptores.

Según los últimos datos del 19 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 784, 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 3.20%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.95% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 117 visualizaciones. En el primer día suele acumular 627 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como |--, algorithm, array, framework, javascript.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 20 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 Tecnologías y Aplicaciones.

66 163

Suscriptores

+124 horas

+1397 días

+78430 días

2 117

Visitas de la publicación

~ 62724 horas

~ 82648 horas

3.20%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

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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