现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Artificial Intelligence
前往频道在 Telegram
🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM
显示更多📈 Telegram 频道 Artificial Intelligence 的分析概览
频道 Artificial Intelligence (@artificial_intelligence_com) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 70 419 名订阅者，在 技术与应用 类别中位列第 1 849，并在 印度 地区排名第 4 785 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 70 419 名订阅者。
根据 13 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 1 217，过去 24 小时变化为 69，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 7.35%。内容发布后 24 小时内通常能获得 2.09% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 5 179 次浏览，首日通常累积 1 474 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 10。
- 主题关注点： 内容集中在 learning, linkedin, linux, udemy, 040k| 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
凭借高频更新（最新数据采集于 14 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
70 419
订阅者
+6924 小时
+2577 天
+1 21730 天
帖子存档
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👨🏻💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
✅ Why should we use it?
🔢 For learning: If you're looking to learn algorithms in action, this is great.
🔢 For practice: You can take the codes, run them, and modify them to better understand.
🔢 For projects : You can even use the codes here in real-life or academic projects.
🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
┌ 🏳️🌈 The Algorithms - Python
└ 🐱 GitHub-Repos
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Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
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📱Artificial Intelligence and Machine Learning
📱Machine Learning Fundamentals for Healthcare
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📂 Full description
Theres an increased demand to integrate AI and machine learning workflows into many different business sectors. This is especially true in todays unique and constantly evolving global healthcare landscape.In this course, instructor Wuraola Oyewusi provides an overview of how AI and machine learning can optimize healthcare processes, data analysis, health outcomes, and more. Along the way, gather insights drawn from real-world examples to address complex privacy and ethical considerations in the industry. Wuraola also shows you how to utilize machine learning for tabular healthcare datasets using a Google Colab Notebook, including clinical records, classification, predictions, regression, clustering, and localization.
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🔅 Machine Learning Fundamentals for Healthcare
🌐 Author: Wuraola Oyewusi
🔰 Level: Beginner
⏰ Duration: 1h 36m
🌀 Get an introduction to the fundamentals of machine learning and AI in this course designed for healthcare professionals.📗 Topics: Healthcare Information Technology, Machine Learning 📤 Join Artificial Intelligence and Machine Learning for more courses
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🔰 The Coding Space
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217k| 🔰 Linkedin Learning Courses
128k| 🔰 Premium Udemy Courses
127k| 🔰 Web Development
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106k| 🔰 Learn Python
095k| 🔰 JavaScript Courses
076k| 🔰 Machine Learning
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065k| 🔰 DevOps Tutorials
059k| 🔰 Learn React and NextJs
057k| 🔰 Data Analysis and Databases
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050k| 🔰 Linux and DevOps
044k| 🔰 100 Days of Python
043k| 🔰 Best Telegram Channels
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040k| 🔰 Business Training
040k| 🔰 ChatGPT Mastery
036k| 🔰 Mobile Development
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035k| 🔰 Zero to Mastery
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029k| 🔰 Crypto Lessons
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