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AI and Machine Learning

AI and Machine Learning

前往频道在 Telegram

Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

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📈 Telegram 频道 AI and Machine Learning 的分析概览

频道 AI and Machine Learning (@machine_learning_courses) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 94 077 名订阅者,在 教育 类别中位列第 1 547,并在 印度 地区排名第 3 005

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 94 077 名订阅者。

根据 26 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 965,过去 24 小时变化为 37,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 6.79%。内容发布后 24 小时内通常能获得 2.34% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 6 384 次浏览,首日通常累积 2 203 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 9
  • 主题关注点: 内容集中在 learning, llm, linkedin, linux, udemy 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:
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

凭借高频更新(最新数据采集于 27 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 教育 类别中的关键影响点。

94 077
订阅者
+3724 小时
+2267
+96530
帖子存档
10. Appendix A Introduction To Cyber Security - Part 02

10. Appendix A Introduction To Cyber Security - Part 01

09. AI Security Risks

08. AI For Malware Detection

07. AI In Network Security - Part 02

07. AI In Network Security - Part 01

06. Building A Phishing Detection System With AI - Part 02

06. Building A Phishing Detection System With AI - Part 01

05. Building An Email Filtering System With AI - Part 03

05. Building An Email Filtering System With AI - Part 02

05. Building An Email Filtering System With AI - Part 01

04. Where Is AI Used In Cyber Security Today - Part 02

04. Where Is AI Used In Cyber Security Today - Part 01

03. New Age Of Social Engineering

02. ChatGPT For Cyber SecurityEthical Hacking

01. Introduction To The Course

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💸 Understanding Popular ML Algorithms: 1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes. 2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not. 3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion. 4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions. 5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs. 6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too! 7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech. 8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things. 9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.