Data Science & Machine Learning
前往频道在 Telegram
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
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📈 Telegram 频道 Data Science & Machine Learning 的分析概览
频道 Data Science & Machine Learning (@datasciencefun) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 77 661 名订阅者，在 教育 类别中位列第 2 015，并在 印度 地区排名第 3 825 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 77 661 名订阅者。
根据 12 十一月, 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 -123，过去 24 小时变化为 -21，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 2.52%。内容发布后 24 小时内通常能获得 1.01% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 954 次浏览，首日通常累积 781 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 learning, accuracy, distribution, panda, dataset 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：
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
凭借高频更新（最新数据采集于 13 十一月, 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。
77 661
订阅者
-2124 小时
-767 天
-12330 天
帖子存档
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Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals PDF
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A LITTLE GUIDE TO HANDLING MISSING DATA
Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀
How can you handle these missing values, ensuring you dont loose important part of your data🤷♀️
Not a problem😌. Here are important facts you must know😉
✍️Instances with missing values for all features should be eliminated
✍️Features with high absence rate should either be eliminated or filled with values
✍️Missing values can be replaced using Mean Imputation or Regression Imputation
✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances
✍️Regression Imputation might overfit your model
✍️Mean and Regression Imputation can't be applied to Text features with missing values
✍️Text Features with missing values can be eliminated if not needed in data
✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized
To become a Machine Learning Engineer:
• Python
• numpy, pandas, matplotlib, Scikit-Learn
• TensorFlow or PyTorch
• Jupyter, Colab
• Analysis > Code
• 99%: Foundational algorithms
• 1%: Other algorithms
• Solve problems ← This is key
• Teaching = 2 × Learning
• Have fun!
#numpy
NumPy
Smart use of ‘:’ to extract the right shape
Sometimes you encounter a 3-dim array that is of shape (N, T, D), while your function requires a shape of (N, D). At a time like this, reshape() will do more harm than good, so you are left with one simple solution:
Example:
for t in xrange(T):
x[:, t, :] = # ...📕 Introduction to Machine Learning
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University Press, Cambridge
Expert_Python_Programming_Master_Python_by_learning_the_best_coding.epub4.52 MB
Pattern Recognition and
Machine Learning [ Information Science and Statistics ]
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#python #machinelearning #statistics #information #ai #ml
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