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Linkedin Learning
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Linkedin Learning Download and watch Linkedin Learning Courses 📈 Pᴀɪᴅ ᴀᴅs : https://telega.io/c/linkedin_learning
Ko'proq ko'rsatish📈 Telegram kanali Linkedin Learning analitikasi
Linkedin Learning (@linkedin_learning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 216 797 obunachidan iborat bo'lib, Taʼlim toifasida 384-o'rinni va Hindiston mintaqasida 717-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 216 797 obunachiga ega bo‘ldi.
29 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -278 ga, so‘nggi 24 soatda esa -117 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 7.49% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.81% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 16 231 marta ko‘riladi; birinchi sutkada odatda 3 932 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 15 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent author, linkedin, linux, javascript, 040k| kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Linkedin Learning
Download and watch Linkedin Learning Courses
📈 Pᴀɪᴅ ᴀᴅs :
https://telega.io/c/linkedin_learning”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 30 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
216 797
Obunachilar
-11724 soatlar
-3367 kunlar
-27830 kunlar
Postlar arxiv
216 765
🔸 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.
216 765
🔅 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 courses216 765
🔸 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.
216 765
🔅 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 courses216 765
🔅 Advanced Python: Working with Databases
🌐 Author: Kathryn Hodge
🔰 Level: Advanced
⏰ Duration: 2h 6m
🌀
Explore the database options for powering your Python apps. Learn how to create and connect to different types of databases, including SQLite, MySQL, and PostgreSQL.
📗 Topics: Databases, Python
📤 Join @python_trainings for more courses216 765
🔅 Using MongoDB with Node.js
🌐 Author: MongoDB
🔰 Level: Intermediate
⏰ Duration: 46m
🌀
Learn how to use MongoDB operations and aggregation, all in Node.js.
📗 Topics: Node.js, MongoDB
📤 Join @JavaScript_Trainings for more courses216 765
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216 765
🎞 Pytube is a lightweight #Python library that enables you to download #YouTube videos and playlists in specific formats and resolutions.
https://github.com/pytube/pytube
216 765
🧿 Python_Learning Channel
🎭 : #Python, #Development
👄 : English
⭐️ : 4.8
🗒 Level up your Python skills with our Telegram channel! Valuable Python insights, tutorials, and community discussions. Let's learn and code together!
216 765
🔸 Full description 🔸
In the new world of work, securely delivering software applications over the internet to students and employees is key to uninterrupted learning and business continuity. In this course, learn how to use Amazon AppStream to keep your remote employees connected to essential data, applications, and resources. Instructor Carlos Rivas explains how to deploy a secure network infrastructure. He then details how to manage and share AppStream images, which are snapshots of one or more software applications that you can later use to deploy as a service to your remote audience. Plus, learn how to properly manage your fleet, generate temporary URLs, set up SSO, and more.
