Machine Learning
رفتن به کانال در Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
نمایش بیشتر
شبکه:📈 تحلیل کانال تلگرام Machine Learning
کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 41 257 مشترک است و جایگاه 3 163 را در دسته فناوری و برنامهها و رتبه 216 را در منطقه سوريا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 41 257 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 14 سپتامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 320 و در ۲۴ ساعت گذشته برابر 14 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 3.04% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.66% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 1 253 بازدید دریافت میکند. در اولین روز معمولاً 683 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند distance, insidead, gpu, learning, degree تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 15 سپتامبر, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
41 257
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| تاریخ | رشد مشترکین | اشارات | کانالها | |
| 15 سپتامبر | +12 | |||
| 14 سپتامبر | +19 | |||
| 13 سپتامبر | +41 | |||
| 12 سپتامبر | +43 | |||
| 11 سپتامبر | +5 | |||
| 10 سپتامبر | +17 | |||
| 09 سپتامبر | +6 | |||
| 08 سپتامبر | +20 | |||
| 07 سپتامبر | +21 | |||
| 06 سپتامبر | +1 | |||
| 05 سپتامبر | +10 | |||
| 04 سپتامبر | +21 | |||
| 03 سپتامبر | +14 | |||
| 02 سپتامبر | +24 | |||
| 01 سپتامبر | +6 |
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| 4 |  🚀Round 2 – 14-Day CCNA & CCNP Study Sprint!
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| 5 |  🎓 Deep Learning for Images with PyTorch: CNNs to GANs
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| 20 |  📋 3 Main Steps for a Proper EDA in Python
🔑 Exploratory Data Analysis Cheat Sheet
From raw data to real insight
EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS.
Good analysis starts with good questions.
🟦 STEP 1 — Data Collection & Structuring
Goal: Get the data in, shape it, and understand its skeleton.
1.1 Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Optional display settings
pd.set_option('display.max_columns', None)
sns.set_style('whitegrid')
1.2 Load the Data File
df = pd.read_csv('data.csv')
# df = pd.read_excel('data.xlsx')
# df = pd.read_sql(query, connection)
df.head() # first rows
df.info() # schema, types, nulls
df.shape # (rows, cols)
df.describe() # quick stats
🟧 STEP 2 — Issue Identification
Goal: Spot problems — missing values, duplicates, wrong types, outliers.
2.1 Missing Values
df.isna().sum() # count per column
df.isna().mean() * 100 # % missing
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df['age'] = df['age'].fillna(df['age'].median())
df['city'] = df['city'].fillna('Unknown')
# Drop
df = df.dropna(subset=['critical_col'])
2.2 Duplicates
df.duplicated().sum()
df = df.drop_duplicates()
2.3 Data Types & Conversions
df.dtypes
df['date'] = pd.to_datetime(df['date'])
df['id'] = df['id'].astype(int)
df['name'] = df['name'].str.strip().str.lower()
2.4 Outliers (IQR method)
Q1 = df['col'].quantile(0.25)
Q3 = df['col'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['col'] < Q1 - 1.5*IQR) | (df['col'] > Q3 + 1.5*IQR)]
sns.boxplot(x=df['col'])
🟩 STEP 3 — Understand & Decide
Goal: Reveal patterns and turn data into decisions.
3.1 Descriptive Statistics
df.describe() # numeric
df['category'].value_counts() # categorical
df['category'].value_counts().plot(kind='bar')
3.2 Data Visualization
# Histogram — distribution
df['age'].hist(bins=30)
# Countplot — categories
sns.countplot(x='category', data=df)
# Scatter — relationships
plt.scatter(df['x'], df['y'])
# Heatmap — correlations
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
3.3 Ask the Right Questions
✅ What's the distribution of each column?
✅ Are there correlations between variables?
✅ Do groups behave differently?
✅ Does the data match business reality?
✅ What story does the data tell?
🟢 Key Notes
💡 Context matters — numbers without meaning mislead
🧹 Quality over quantity — clean data beats big data
🧠 Understand before you predict — EDA is the foundation of every successful data project
🎯 The 3-Step Formula
Step
Focus
Key Action
1
Collect & Structure
Load + shape the data
2
Identify Issues
Missing, duplicates, outliers, types
3
Understand & Decide
Stats + viz + right questions
EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS.
Better data → Better decisions* | 5 |
