¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
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
Mostrar más📈 Análisis del canal de Telegram Machine Learning
El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 100 suscriptores, ocupando la posición 3 398 en la categoría Tecnologías y Aplicaciones y el puesto 232 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 100 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 379, y en las últimas 24 horas de 30, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.92%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 770 visualizaciones. En el primer día suele acumular 466 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
40 100
Suscriptores
+3024 horas
+337 días
+37930 días
Archivo de publicaciones
40 087
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 file2. 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 names3. DATA SELECTION
Method | What it does -----------+---------------------------------- df.loc[] | Selection by row and column names df.iloc[] | Selection by indices df.query() | Filtering by condition4. 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 type5. 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 columns6. 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()
40 087
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()40 087
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼
The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]s = pd.Series([10, 15, 20, 25, 30])
mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]40 087
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A free MIT guide to key computer vision concepts 📘
Link: https://visionbook.mit.edu/ 🔗
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40 087
Repost from Machine Learning with Python
Learn AI for free directly from top companies. 🚀
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn
💬 Comment "Learning" if you find this helpful.
🔄 Repost so others can take help.
🔖 Must bookmark for future reference.
#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://t.me/CodeProgrammer
40 087
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40 087
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