Data Science & Machine Learning Resources

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 Admin: @love_data Buy ads: https://telega.io/c/datalemur

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网络:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert 印度20 482 教育9 687...

📈 Telegram 频道 Data Science & Machine Learning Resources 的分析概览

频道 Data Science & Machine Learning Resources (@datalemur) 英语语言赛道中的是活跃参与者。目前社区聚集了 20 597 名订阅者，在教育类别中位列第 9 687，并在印度地区排名第 20 482 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 20 597 名订阅者。

根据 30 七月, 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 127，过去 24 小时变化为 6，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 2.66%。内容发布后 24 小时内通常能获得 0.68% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 547 次浏览，首日通常累积 140 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 2。
主题关注点： 内容集中在 |--, learning, insidead, database, sql 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 Admin: @love_data Buy ads: https://telega.io/c/datalemur”

凭借高频更新（最新数据采集于 31 七月, 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。

20 597

订阅者

+624 小时

+447 天

+12730 天

547

帖子浏览量

~ 14024 小时

~ 20148 小时

2.66%

参与率

无数据

每日帖子数

Ads index

beta

数据加载中...

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吸引订阅者

七月 '26

+239

在3个频道中

六月 '26

+150

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五月 '26

+344

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四月 '26

+188

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三月 '26

+262

在1个频道中

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二月 '26

+433

在1个频道中

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一月 '26

+637

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十二月 '25

+494

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十一月 '25

+630

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十月 '25

+650

在1个频道中

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九月 '25

+656

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八月 '25

+839

在4个频道中

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七月 '25

+1 110

在6个频道中

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六月 '25

+1 989

在9个频道中

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五月 '25

+2 717

在7个频道中

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+3 878

在5个频道中

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三月 '25

+1 119

在3个频道中

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+1 084

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一月 '25

+1 436

在5个频道中

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十二月 '24

+132

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十一月 '24

+551

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十月 '24

+648

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九月 '24

+1 255

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八月 '24

+162

在1个频道中

日期	订阅者增长	提及	频道
31 七月	+8
30 七月	+6
29 七月	+14
28 七月	+9
27 七月	+10
26 七月	+1
25 七月	+12
24 七月	+10
23 七月	+8
22 七月	+12
21 七月	+14
20 七月	+16
19 七月	+12
18 七月	+7
17 七月	+8
16 七月	+5
15 七月	+11
14 七月	+2
13 七月	+8
12 七月	+2
11 七月	+5
10 七月	+1
09 七月	+5
08 七月	+9
07 七月	+4
06 七月	+12
05 七月	+7
04 七月	+18
03 七月	0
02 七月	+3
01 七月	0

频道帖子

Data Science Roadmap: 🗺 📂 Math & Stats ∟📂 Python/R ∟📂 Data Wrangling ∟📂 Visualization ∟📂 ML ∟📂 DL & NLP ∟📂 Projects ∟ ✅ Apply For Job Like if you need detailed explanation step-by-step ❤️

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3	Top 10 machine Learning algorithms 👇👇 1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output. 2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class. 3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure. 4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees. 5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes. 6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set. 7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label. 8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training. 9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors. 10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data. Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊	576
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