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Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9

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The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab

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Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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Master Python with step-by-step courses – from basics to advanced projects and practical applications.
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Important SQL concepts to master.pdf

3 MB

Important #SQL concepts to master:
- Joins (inner, left, right, full)
- Group By vs Where vs Having
- Window functions (ROW_NUMBER, RANK, DENSE_RANK)
- CTEs (Common Table Expressions)
- Subqueries and nested queries
- Aggregations and filtering
- Indexing and performance basics
- NULL handling

Interview Tips:
- Focus on writing clean, readable queries
- Explain your logic clearly don’t just jump to #code
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A comprehensive summary of the Seaborn Library.pdf

3.3 MB

📊

A comprehensive summary of the «Seaborn Library»

👨🏻‍💻 One of the best choices for any data scientist to convert data into clear and beautiful charts, so that they can better understand what the data is saying and also be able to present the results correctly and clearly to others, is the Seaborn library.

✅ A very user-friendly library for creating professional charts with minimal coding. It is built on top of Matplotlib but is simpler and easier to use than that.

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A comprehensive summary of the Seaborn Library.pdf

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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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Pyspark Functions.pdf

4.1 MB

M𝗼𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘂𝘀𝗲 #𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆… 𝗯𝘂𝘁 𝗳𝗲𝘄 𝗸𝗻𝗼𝘄 𝘄𝗵𝗶𝗰𝗵 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲.

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• Core Ops: select(), withColumn(), filter(), dropDuplicates()
• Aggregations: groupBy(), countDistinct(), collect_list()
• Strings: concat(), split(), regexp_extract(), trim()
• Window: row_number(), rank(), lead(), lag()
• Date/Time: current_date(), date_add(), last_day(), months_between()
• Arrays/Maps: array(), array_union(), MapType

Just mastering these ~20 functions can simplify 70% of your transformations.

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Forwarded from Machine Learning with Python

Numpy @CodeProgrammer.pdf

2.4 MB

🏷

Sections of the «NumPy» library
⬅️ From introductory to advanced

👨🏻‍💻 This is a long-term project to learn Python and NumPy from scratch. The main task is to handle numerical #data and #arrays in #Python using NumPy, and many other libraries are also used.

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⭕️ Introduction to NumPy
🟠 NumPy arrays
⭕️ Introduction to array features
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SQL Ultimate Cheat Sheet

Standard #SQL, Queries & Management

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Forwarded from Machine Learning with Python

🚀 #Pandas Cheat Sheet for Everyday Data Work

This covers the essential functions we use in day to day work like inspecting data, selecting rows and columns, cleaning, manipulating and doing quick aggregations.

https://news.1rj.ru/str/CodeProgrammer

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Forwarded from Machine Learning with Python

Mastering pandas%22.pdf

1.6 MB

🌟

A new and comprehensive book "Mastering pandas"

👨🏻‍💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.

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Python Libraries You Should Know ✅

⦁ NumPy: Numerical Computing ⚙️
NumPy is the foundation for numerical operations in Python. It provides fast arrays and math functions.

Example:

import numpy as np

arr = np.array([1, 2, 3])
print(arr * 2)  # [2 4 6]

Challenge: Create a 3x3 matrix of random integers from 1–10.

matrix = np.random.randint(1, 11, size=(3, 3))
print(matrix)

⦁ Pandas: Data Analysis 🐼
Pandas makes it easy to work with tabular data using DataFrames.

Example:

import pandas as pd

data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)
print(df)

Challenge: Load a CSV file and show the top 5 rows.

df = pd.read_csv("data.csv")
print(df.head())

⦁ Matplotlib: Data Visualization 📊
Matplotlib helps you create charts and plots.

Example:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [2, 4, 1]

plt.plot(x, y)
plt.noscript("Simple Line Plot")
plt.show()

Challenge: Plot a bar chart of fruit sales.

fruits = ["Apples", "Bananas", "Cherries"]
sales = [30, 45, 25]

plt.bar(fruits, sales)
plt.noscript("Fruit Sales")
plt.show()

⦁ Seaborn: Statistical Plots 🎨
Seaborn builds on Matplotlib with beautiful, high-level charts.

Example:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Challenge: Create a heatmap of correlation.

corr = tips.corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.show()

⦁ Requests: HTTP for Humans 🌐
Requests makes it easy to send HTTP requests.

Example:

import requests

response = requests.get("https://api.github.com")
print(response.status_code)
print(response.json())

Challenge: Fetch and print your IP address.

res = requests.get("https://api.ipify.org?format=json")
print(res.json()["ip"])

⦁ Beautiful Soup: Web Scraping 🍜
Beautiful Soup helps you extract data from HTML pages.

Example:

from bs4 import BeautifulSoup
import requests

url = "https://example.com"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")

print(soup.noscript.text)

Challenge: Extract all links from a webpage.

links = soup.find_all("a")
for link in links:
    print(link.get("href"))

Next Steps:
⦁ Combine these libraries for real-world projects
⦁ Try scraping data and analyzing it with Pandas
⦁ Visualize insights with Seaborn and Matplotlib

Double Tap ♥️ For More

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🎯

PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ

💾

Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1

🧑‍🎓

Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC

😀

ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT

💬

Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9

🐍

Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab

🖊

📺

Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV

📈

Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX

🎧

Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53

⭐️

Research Papers
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY

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These Python commands cover 90% of data cleaning tasks you'll ever need 👇

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