Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
显示更多📈 Telegram 频道 Machine Learning & Artificial Intelligence | Data Science Free Courses 的分析概览
频道 Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 66 762 名订阅者,在 教育 类别中位列第 2 446,并在 马来西亚 地区排名第 431 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 66 762 名订阅者。
根据 25 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 519,过去 24 小时变化为 31,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 0.76%。内容发布后 24 小时内通常能获得 0.78% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 510 次浏览,首日通常累积 524 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 3。
- 主题关注点: 内容集中在 sellerflash, waybienad, pricing, buybox, buyer 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:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
凭借高频更新(最新数据采集于 26 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。
66 762
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+3124 小时
+797 天
+51930 天
帖子存档
Python Machine Learning .pdf1.42 MB
50 SQL Interview questions
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SQL for Data Analysis
Автор: Cathy Tanimura
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Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:
👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.
👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.
👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.
👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.
👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.
By following these tips, you can be well-prepared for your next data science interview. Good luck!
现已上线!2025 年 Telegram 研究 — 年度关键洞察 
