Linkedin Learning

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Linkedin Learning Download and watch Linkedin Learning Courses 📈 Pᴀɪᴅ ᴀᴅs : https://telega.io/c/linkedin_learning

📈 Аналітичний огляд Telegram-каналу Linkedin Learning

Канал Linkedin Learning (@linkedin_learning) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 217 142 підписників, посідаючи 389 місце в категорії Освіта та 717 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 217 142 підписників.

За останніми даними від 21 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 210, а за останні 24 години на -2, загальне охоплення залишається високим.

Статус верифікації: Не верифікований
Рівень залученості (ER): Середній показник залученості аудиторії становить 6.71%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.41% реакцій від загальної кількості підписників.
Охоплення публікацій: В середньому кожен допис отримує 14 571 переглядів. Протягом першої доби публікація в середньому набирає 3 053 переглядів.
Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 13.
Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як author, linkedin, linux, javascript, 040k|.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Linkedin Learning Download and watch Linkedin Learning Courses 📈 Pᴀɪᴅ ᴀᴅs : https://telega.io/c/linkedin_learning”

Завдяки високій частоті оновлень (останні дані отримано 22 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

217 142

Підписники

-224 години

+27 днів

+21030 день

14 571

Перегляди допису

~ 3 05324 години

~ 4 50148 годин

6.71%

Коефіцієнт залучення

Немає даних

Дописів на день

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Архів дописів

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🔸 Full description 🔸 In the world of hybrid work, uncertainty, and constant change, people need healthy work relationships to feel supported and show up as their best selves, This course teaches essential skills to build and maintain more resilient relationships. Dr. Emily Anhalt teaches essential tools and skills, including: taking ownership of your part, strategies to build empathy, ways to create a more positive and safe working environment, ways to move from reactive to proactive, how to have healthy conflict, getting on the same team, expressing yourself more effectively, and breaking an emotional sweat every day.

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🔅 Skills to Build Stronger Work Relationships 🌐 Author: Emily Anhalt 🔰 Level: General ⏰ Duration: 36m 🌀 Learn essential skills to build stronger work relationships to navigate stresses and increase joy and success. 📗 Topics: Interpersonal Relationships, Positive Work Environment 📤 Join @linkedin_learning for more courses

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🔸 Full description 🔸 JavaScript is a scripting language of the web. As the web evolves from a static to a dynamic environment, technology focus is shifting from static markup and styling to dynamic interfaces and advanced interaction. Once seen as optional, JavaScript is now becoming an integral part of the web, infusing every layer with its script.This course includes Code Challenges powered by CoderPad. Code Challenges are interactive coding exercises with real-time feedback, so you can get hands-on coding practice alongside the course content to advance your programming skills. Instructor Morten Rand-Hendriksen helps you build your understanding of JavaScript piece by piece, from core principles like variables, data types, conditionals, and functions through advanced topics including loops, and DOM scripting.

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🔅 JavaScript Essential Training 🌐 Author: Morten Rand-Hendriksen 🔰 Level: Beginner ⏰ Duration: 5h 40m 🌀

Get hands-on experience scripting in JavaScript. Learn how to work with objects, variables, data types, conditionals, functions, loops, the DOM, and more.

📗 Topics: JavaScript 📤 Join @linkedin_learning for more courses

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AutoML: Build Production-Ready Models Quickly! @Linkedin_Learning

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🔸 Full description 🔸 Suggested prerequisites Python Essential Training Python for Data Science Essential Training Python Essential Libraries Projects Build a baseline AutoGluon model to predict how long flights will be delayed using a flight-delay prediction dataset. Using Azure Custom Vision, perform advanced training to build a model that can classify images of different African fashion styles. Perform multi-class text classification on AG's news topic classification dataset using Hugging Face AutoTrain. Join machine learning engineer Akintunde Oluwatobiloba Oladipo as he shows you how to build production-ready ML models quickly and accurately, using automation to optimize your workflows. Test out your new skills along the way with hands-on practice challenges. By the end of this course, youll be prepared to set bigger and bolder business objectives, using AutoML modeling tools as your analytic guide.

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🔅 AutoML: Build Production-Ready Models Quickly! 🌐 Author: Akintunde Oluwatobiloba Oladipo 🔰 Level: Intermediate ⏰ Duration: 1h 44m 🌀 Learn the basics of building production-ready automated machine learning (AutoML) models. 📗 Topics: Automated Machine Learning 📤 Join @linkedin_learning for more courses

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Applied Machine Learning: Ensemble Learning @Linkedin_Learning

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🔸 Full description 🔸 Do you want to grow your skills as a machine learning practitioner, but dont know where to begin? You dont need any formal training in data science to start working toward your goal. In this course, instructor Derek Jedamski shows you how to harness messy data, find signal in it, and build models that make powerful predictions with ensemble learners, one of the most common classes of machine learning algorithms.Review the basics of the machine learning pipeline to find out where ensemble learners sit within it. Learn about the underlying theory that drives ensemble learners, covering examples of ensemble learning in Python and then implementing models of your own. Explore concepts like boosting, bagging, and stacking, and how to use each and when. Get the tools you need to ramp up your predicting power and advance your machine learning skills today.

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🔅 Applied Machine Learning: Ensemble Learning 🌐 Author: Derek Jedamski 🔰 Level: Intermediate ⏰ Duration: 2h 25m 🌀

Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.

📗 Topics: Applied Machine Learning 📤 Join @linkedin_learning for more courses