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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Automatic translator in Python!
We translate a text in a few lines using deep-translator. It supports dozens of languages: from English and Russian to Japanese and Arabic.
Install the library:
pip install deep-translator
from deep_translator import GoogleTranslator
text = "Hello, how are you?"
result = GoogleTranslator(source="ru", target="en").translate(text)
print("Original:", text)
print("Translation:", result)
texts = ["Hello", "What's your name?", "See you later"]
for t in texts:
print("→", GoogleTranslator(source="ru", target="es").translate(t))
🔥 NEW YEAR 2026 – PREMIUM SCIENTIFIC PAPER WRITING OFFER 🔥
Q1-Ready | Journal-Targeted | Publication-Focused
Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only.
To start 2026 strong, we’re offering a limited New Year scientific writing package designed for fast-track publication, not academic busywork.
🎯 What We Offer (End-of-Year Special):
✍️ Full Research Paper Writing – $400
(Q1 / Q2 journal–ready)
Includes:
✅ Journal-targeted manuscript (Elsevier / Springer / Wiley / IEEE / MDPI)
✅ IMRAD structure (Introduction–Methods–Results–Discussion)
✅ Strong problem formulation & novelty framing
✅ Methodology written to reviewer standards
✅ Professional academic English (native-level)
✅ Plagiarism-free (Turnitin <10%)
✅ Ready for immediate submission
📊 Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
🧠 Optional Add-ons (if needed):
Journal selection & scope matching
Cover letter to editor
Reviewer response (after review)
Statistical validation & result polishing
Figure & table redesign (publication quality)
🚀 Why This Is Different
We don’t “write generic papers.”
We engineer publishable research.
✔️ Real novelty positioning
✔️ Reviewer-proof logic
✔️ Data-driven arguments
✔️ Aligned with current 2025–2026 journal expectations
Many of our papers are built on real-world datasets and are already aligned with Q1 journal standards.
⏳ New Year Offer – Limited Time
Regular price: $1,500 – $3,000
New Year 2026 price: $400
Limited slots (quality > quantity)
🎓 Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
📩 DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paper—not just a plan
I'm happy to announce that freeCodeCamp has launched a new certification in #Python 🐍
» Learning the basics of programming
» Project development
» Final exam
» Obtaining a certificate
Everything takes place directly in the browser, without installation. This is one of the six certificates in version 10 of the Full Stack Developer training program.
Full announcement with a detailed FAQ about the certificate, the course, and the exams
Link: https://www.freecodecamp.org/news/freecodecamps-new-python-certification-is-now-live/
👉 @codeprogrammer
It's both funny and sad... #memes
➡ @codeprogrammer
The #Python library #PandasAI has been released for simplified data analysis using AI.
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link 😍
👉 /channel/CodeProgrammer
Machine Learning Fundamentals
A structured Machine Learning Fundamentals guide covering core concepts, intuition, math basics, ML algorithms, deep learning, and real-world workflows.
/channel/CodeProgrammer 🎀
Real Python - Pocket Reference (Important)
#python #py #PythonTips #programming
/channel/CodeProgrammer 🩵
Data Cleaning & Preprocessing Cheat Sheet
Essential Steps: Inputs, Outputs & Code
/channel/CodeProgrammer 💙
❗️LISA HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY!
Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel
/channel/+YDWOxSLvMfQ2MGNi
⚡️FREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! 👆👇
/channel/+YDWOxSLvMfQ2MGNi
🚀 #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.
/channel/CodeProgrammer ❤️
🏷 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.
✏️ This section shows a structured and complete path for learning #NumPy; but the code examples and exercises help to practically memorize the concepts.
⭕️ Introduction to NumPy
🟠 NumPy arrays
⭕️ Introduction to array features
🟠 Basic operations on arrays
⭕️ Functions for statistical and aggregative purposes
🟠 And...
/channel/CodeProgrammer ⛈⚡️
📊 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.
✏️ With this summary, you will learn the syntax, see many examples and real applications of #Seaborn, and ultimately help you elevate your #datavisualization skills by several levels.
🌐 #Data_Science #DataScience
/channel/DataAnalyticsX 🌟
React 💖 for more amazing content
🔖 Sharing a handy cheat sheet on 12 key Git commands — this is the basic set that no developer can do without
tags: #cheatsheet #git
/channel/CodeProgrammer 🔗
🚀 Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
/channel/CodeProgrammer
🔖 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.
/channel/DataScienceM
🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
/channel/DataScience4
🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
/channel/DataScienceQ
💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
/channel/datasets1
🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
/channel/DataScienceC
😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
/channel/DataScienceT
💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
/channel/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
/channel/PythonArab
🖊 Data Science Jupyter Notebooks
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.
/channel/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
/channel/DataScienceV
📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
/channel/DataAnalyticsX
🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
/channel/Python53
⭐️ Research Papers
Professional Academic Writing & Simulation Services
/channel/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
🏷 "Statistics for Data Science" Notes
👨🏻💻 In these notes, everything is structured and neatly organized from the basics of statistics to advanced tips. Each concept is explained with examples, formulas, and charts to make learning easy
🛑 What is statistics and why is it important?
🛑 Basic concepts
🛑 Descriptive statistics
🛑 Inferential statistics
🛑 Discrete and continuous distributions
🛑 And many other topics
🌐 #Data_Science #DataScience
/channel/CodeProgrammer ✅
React ♥️ for more amazing content
🚀Stanford just completed a must-watch for anyone serious about AI:
🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.
If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)
🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
🎥 Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.
Let’s level up.
/channel/CodeProgrammer 😅
1. What will be the output of the following code?
def add_item(item, lst=None):
if lst is None:
lst = []
lst.append(item)
return lst
print(add_item(1))
print(add_item(2))
x = 10
def func():
print(x)
x = 5
func()
a = [1, 2, 3]
b = a[:]
a.append(4)
print(b)
bool("False")print(type({}))x = (1, 2, [3])
x[2] += [4]
print(x)
print([i for i in range(3) if i])
d = {"a": 1}
print(d.get("b", 2))print(1 in [1, 2], 1 is 1)
def gen():
for i in range(2):
yield i
g = gen()
print(next(g), next(g))
print({x: x*x for x in range(2)})print([] == [], [] is [])
def f():
try:
return "A"
finally:
print("B")
print(f())
x = [1, 2]
y = x
x = x + [3]
print(y)
print(type(i for i in range(3)))
100+ LLM Interview Questions and Answers (GitHub Repo)
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
/channel/DataScienceM ✅
Convert any long article or PDF into a test in a couple of seconds!
Mini-service: we take the text of the article (or extract it from PDF), send it to GPT and receive a set of test questions with answer options and a key.
First, we load the text of the material:
# article_text — this is where we put the text of the article
with open("article.txt", "r", encoding="utf-8") as f:
article_text = f.read()
# for PDF, you can extract the text in advance with any library (PyPDF2, pdfplumber, etc.)
GPT to generate a test:prompt = (
"You are an exam methodologist."
"Based on this text, create 15 test questions."
"Each question is in the format:\n"
"1) Question text\n"
"A. Option 1\n"
"B. Option 2\n"
"C. Option 3\n"
"D. Option 4\n"
"Correct answer: <letter>."
"Do not add explanations and comments, only questions, options, and correct answers."
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": article_text}
])
print(response.choices[0].message.content.strip())
I rarely say this, but this is the best repository for mastering Python.
The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).
In this PythonMastery.pdf, all the information is structured
👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf
In the Exercises folder, all the exercises are located
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises
In the Solutions folder — the solutions
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions
👉 @codeprogrammer
Want to get into Data Analysis?
Here are paid courses with certificates to build real skills:
1️⃣ Google Data Analytics Certificate
https://lnkd.in/dqEU-yht
2️⃣ IBM Data Science Certificate
https://lnkd.in/dQz58dY6
3️⃣ SQL Basics for Data Science
https://lnkd.in/dcFHHm28
4️⃣ Google Business Intelligence Certificate
https://lnkd.in/d4gbdF24
5️⃣ Microsoft Python Development Certificate
https://lnkd.in/dDXX_AHM
Which data skill are you focusing on now?
🌟 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.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
/channel/CodeProgrammer ⚡️
𝗜𝗳 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗶𝗻 𝘁𝗼𝘀𝘀𝗲𝘀…
Think again! 🎲
Here’s why it’s a game-changer for anyone in data science, analytics, and decision-making:
➜ Decode Uncertainty
From weather forecasts to financial markets, probability helps us make smarter choices.
➜ Master Essential Distributions
Understand Binomial, Poisson, Normal, and more in the simplest way possible.
➜ Crack Data Science Interviews
#Probability is a key topic in analytics and #machinelearning interviews.
➜ Avoid Common Misconceptions
Learn why "50-50 odds" don’t always mean a fair game.
➜ Visualize Concepts, Not Just Formulas
The best way to learn is through intuitive graphs and real-world examples!
/channel/CodeProgrammer ✅
If you want to truly understand how AI systems like #GPT, #Claude, #Llama or #Mistral work at their core, these 85 foundational concepts are essential. The visual below breaks down the most important ideas across the full #AI and #LLM landscape.
/channel/CodeProgrammer ✅
🎓 OSSU Computer Science — your free Computer Science degree
Open Source Society University (OSSU) is a complete Computer Science curriculum compiled from the best free courses in the world (MIT, Harvard, Princeton). This is not just random videos, but a structured study plan for 2-3 years: from introduction and algorithms to operating systems, databases, and machine learning.
What is important:
➡️ A full Curriculum — the program follows the standard undergraduate CS degree: mathematics, algorithms, architecture, OS, networks, security.
➡️ Top sources — courses from MIT, Stanford, Google, Princeton, and other giants.
➡️ Final Project — at the end of the program, you create a big project to consolidate your knowledge in practice.
🎁❗️TODAY FREE❗️🎁
Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥
JOIN 👇
/channel/+MPpZ4FO2PHQ4OTZi
/channel/+MPpZ4FO2PHQ4OTZi
/channel/+MPpZ4FO2PHQ4OTZi
🚀 Pass Your IT Exam in 2025——Free Practice Tests & Premium Materials
SPOTO offers free, instant access to high-quality, up-to-date resources that help you study smarter and pass faster
✔️ Python, CCNA, CCNP, AWS, PMP, CISSP, Azure, & more
✔️ 100% Free, no sign-up, Instantly downloadable
📥Grab your free materials here:
·IT exams skill Test : https://bit.ly/443t4xB
·IT Certs E-book : https://bit.ly/4izDv1D
·Python, Excel, Cyber Security Courses : https://bit.ly/44LidZf
📱 Join Our IT Study Group for insider tips & expert support:
https://chat.whatsapp.com/K3n7OYEXgT1CHGylN6fM5a
💬 Need help ? Chat with an admin now:
wa.link/cbfsmf
⏳ Don’t Wait—Boost Your Career Today!
📕 #pandas Cheat Sheet
👨🏻💻 To easily read, inspect, clean, and manipulate data however you want, you need to master pandas!
✏️ To make learning and using pandas easier, this #cheatsheet covers almost all the important features you need for data-driven projects.
✔️ Reading and writing data
✔️ Data inspection
✔️ Data transformation and cleaning
✔️ Grouping and summarizing
✔️ Combining datasets
🌐 #DataScience #DataScience
/channel/DataAnalyticsX 🏐
🚀 𝐄𝐩𝐨𝐜𝐡 𝐯𝐬 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 – 𝐓𝐡𝐞 𝐌𝐨𝐬𝐭 𝐂𝐨𝐦𝐦𝐨𝐧𝐥𝐲 𝐂𝐨𝐧𝐟𝐮𝐬𝐞𝐝 𝐓𝐞𝐫𝐦𝐬!
When training a neural network, two words confuse most beginners:
🔹 𝐄𝐩𝐨𝐜𝐡
𝘈𝘯 𝘦𝘱𝘰𝘤𝘩 𝘮𝘦𝘢𝘯𝘴 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘩𝘢𝘴 𝘴𝘦𝘦𝘯 𝘵𝘩𝘦 𝘦𝘯𝘵𝘪𝘳𝘦 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 𝘰𝘯𝘤𝘦.
𝘐𝘧 𝘺𝘰𝘶 𝘵𝘳𝘢𝘪𝘯 𝘧𝘰𝘳 10 𝘦𝘱𝘰𝘤𝘩𝘴, 𝘺𝘰𝘶𝘳 𝘮𝘰𝘥𝘦𝘭 𝘨𝘰𝘦𝘴 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘵𝘩𝘦 𝘸𝘩𝘰𝘭𝘦 𝘥𝘢𝘵𝘢 10 𝘵𝘪𝘮𝘦𝘴.
🔹 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧
𝘈𝘯 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘪𝘴 𝘰𝘯𝘦 𝘸𝘦𝘪𝘨𝘩𝘵 𝘶𝘱𝘥𝘢𝘵𝘦, 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘣𝘢𝘵𝘤𝘩 𝘰𝘧 𝘥𝘢𝘵𝘢.
If you have:
10,000 𝘳𝘦𝘤𝘰𝘳𝘥𝘴
𝘉𝘢𝘵𝘤𝘩 𝘴𝘪𝘻𝘦 = 100
👉 𝘛𝘩𝘦𝘯 𝘺𝘰𝘶 𝘨𝘦𝘵 100 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴 𝘱𝘦𝘳 𝘦𝘱𝘰𝘤𝘩.
✔️ 𝐒𝐢𝐦𝐩𝐥𝐞 𝐀𝐧𝐚𝐥𝐨𝐠𝐲
𝐓𝐡𝐢𝐧𝐤 𝐨𝐟 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐭𝐡𝐞 𝐠𝐲𝐦:
𝐄𝐩𝐨𝐜𝐡 = 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐰𝐨𝐫𝐤𝐨𝐮𝐭 𝐩𝐥𝐚𝐧 𝐨𝐧𝐜𝐞
𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 = 𝐝𝐨𝐢𝐧𝐠 𝐨𝐧𝐞 𝐬𝐞𝐭 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐨𝐮𝐭
The model becomes stronger with every iteration, and improves overall with more epochs.
Read More: https://telegra.ph/Demystifying-Epochs--Iterations-in-Deep-Learning-11-24
👉 @codeprogrammer