Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Network:Machine Learning with Python Syria231 Technologies & Applications3 380...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 134 subscribers, ranking 3 380 in the Technologies & Applications category and 231 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 134 subscribers.

According to the latest data from 25 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 395 over the last 30 days and by 12 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.89%. Within the first 24 hours after publication, content typically collects 1.31% reactions from the total number of subscribers.
Post reach: On average, each post receives 758 views. Within the first day, a publication typically gains 525 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 26 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

40 134

Subscribers

+1224 hours

+697 days

+39530 days

758

Post views

~ 52524 hours

~ 70348 hours

1.89%

Engagement rate

~ 3

Posts per day

Ads index

beta

Data loading in progress...

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Incoming and Outgoing Mentions

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Attracting Subscribers

June '26

+523

in 4 channels

May '26

+748

in 10 channels

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April '26

+423

in 8 channels

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March '26

+286

in 7 channels

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February '26

+467

in 12 channels

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January '26

+624

in 13 channels

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December '25

+678

in 14 channels

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November '25

+642

in 3 channels

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October '25

+1 052

in 9 channels

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September '25

+1 012

in 8 channels

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August '25

+470

in 3 channels

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July '25

+1 113

in 14 channels

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June '25

+876

in 5 channels

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May '25

+1 030

in 2 channels

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April '25

+1 368

in 2 channels

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March '25

+820

in 1 channels

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February '25

+1 068

in 6 channels

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January '25

+1 062

in 4 channels

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December '24

+895

in 4 channels

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November '24

+1 359

in 14 channels

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October '24

+1 841

in 14 channels

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September '24

+1 406

in 16 channels

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August '24

+1 739

in 13 channels

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July '24

+1 108

in 14 channels

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June '24

+1 335

in 12 channels

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May '24

+1 257

in 15 channels

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April '24

+1 349

in 7 channels

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March '24

+1 778

in 11 channels

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February '24

+1 882

in 12 channels

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January '24

+2 482

in 16 channels

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December '23

+1 638

in 17 channels

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November '23

+1 420

in 14 channels

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October '23

+811

in 15 channels

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September '23

+1 076

in 0 channels

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August '23

+1 246

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July '23

+4 094

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June '23

+1 831

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May '23

+1 304

in 0 channels

Date	Subscriber Growth	Mentions	Channels
26 June	+18
25 June	+18
24 June	+38
23 June	+34
22 June	+9
21 June	+10
20 June	+10
19 June	+8
18 June	+13
17 June	+17
16 June	+23
15 June	+33
14 June	+24
13 June	+14
12 June	+27
11 June	+12
10 June	+20
09 June	+8
08 June	+14
07 June	+18
06 June	+17
05 June	+24
04 June	+24
03 June	+26
02 June	+33
01 June	+31

Channel Posts

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10	PANDAS — CHEAT SHEET 1. DATA LOADING Method \| What it does ----------------+-------------------- pd.read_csv() \| Reads CSV file pd.read_excel() \| Reads Excel file pd.read_sql() \| Reads data from SQL pd.read_json() \| Reads JSON file 2. DATA ANALYSIS Method \| What it does --------------+--------------------------- df.head() \| Shows first rows df.info() \| Table information df.describe() \| Statistics by columns df.shape \| Table size (rows, columns) df.columns \| List of column names 3. DATA SELECTION Method \| What it does -----------+---------------------------------- df.loc[] \| Selection by row and column names df.iloc[] \| Selection by indices df.query() \| Filtering by condition 4. DATA CLEANING Method \| What it does ---------------------+-------------------------------- df.isnull() \| Check for missing values (NULL) df.dropna() \| Remove rows with missing values df.fillna() \| Fill missing values df.drop_duplicates() \| Remove duplicates df.astype() \| Change data type 5. ANALYTICS Method \| What it does ------------------+---------------------------- df.groupby() \| Data grouping df.agg() \| Aggregation in groups df.value_counts() \| Count of unique values df.mean() \| Mean value df.median() \| Median df.corr() \| Correlation between columns 6. DATA MERGING Method \| What it does ------------+--------------------- pd.merge() \| SQL JOIN by column pd.join() \| JOIN by index pd.concat() \| Glue tables together ⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()	1 064
11	PANDAS — CHEAT SHEET 1. DATA LOADING Method \| What it does ----------------+-------------------- pd.read_csv() \| Reads CSV file pd.read_excel() \| Reads Excel file pd.read_sql() \| Reads data from SQL pd.read_json() \| Reads JSON file 2. DATA ANALYSIS Method \| What it does --------------+--------------------------- df.head() \| Shows first rows df.info() \| Table information df.describe() \| Statistics by columns df.shape \| Table size (rows, columns) df.columns \| List of column names 3. DATA SELECTION Method \| What it does -----------+---------------------------------- df.loc[] \| Selection by row and column names df.iloc[] \| Selection by indices df.query() \| Filtering by condition 4. DATA CLEANING Method \| What it does ---------------------+-------------------------------- df.isnull() \| Check for missing values (NULL) df.dropna() \| Remove rows with missing values df.fillna() \| Fill missing values df.drop_duplicates() \| Remove duplicates df.astype() \| Change data type 5. ANALYTICS Method \| What it does ------------------+---------------------------- df.groupby() \| Data grouping df.agg() \| Aggregation in groups df.value_counts() \| Count of unique values df.mean() \| Mean value df.median() \| Median df.corr() \| Correlation between columns 6. DATA MERGING Method \| What it does ------------+--------------------- pd.merge() \| SQL JOIN by column pd.join() \| JOIN by index pd.concat() \| Glue tables together ⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()	1
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