Today I am 3️⃣0️⃣ years old, I am excited to make more successes and achievements
My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward
Best regards
Eng. @HusseinSheikho 🔤
My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward
Best regards
Eng. @HusseinSheikho 🔤
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Harvard's "Advanced Algorithms"
by Jelani Nelson
📽 Lecture Videos: https://youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf
📷 Lecture Notes: https://people.seas.harvard.edu/~cs224/fall14/lec.html
https://t.iss.one/CodeProgrammer 👍
by Jelani Nelson
📽 Lecture Videos: https://youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf
📷 Lecture Notes: https://people.seas.harvard.edu/~cs224/fall14/lec.html
https://t.iss.one/CodeProgrammer 👍
❤6
Google Collab notebooks to learn everything you need to master prompt engineering with Claude - from basic structure and role prompting to advanced techniques like few-shot learning, avoiding hallucinations, and tool use.
Perfect interactive lessons to level up your AI skills
Link: https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P
https://t.iss.one/CodeProgrammer
Perfect interactive lessons to level up your AI skills
Link: https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P
https://t.iss.one/CodeProgrammer
❤6👍4
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This GitHub repository is a real treasure trove of free programming books.
Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more✋
GitHub: https://github.com/EbookFoundation/free-programming-books
https://t.iss.one/CodeProgrammer⭐
Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more
GitHub: https://github.com/EbookFoundation/free-programming-books
https://t.iss.one/CodeProgrammer
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Forwarded from Data Science Machine Learning Data Analysis
📌 Image Segmentation With K-Means Clustering
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read
An introduction with Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read
An introduction with Python
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Forwarded from Data Science Machine Learning Data Analysis
📌 A Guide to Clustering Algorithms
🗂 Category: DATA SCIENCE
🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read
An overview of clustering and the different families of clustering algorithms.
🗂 Category: DATA SCIENCE
🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read
An overview of clustering and the different families of clustering algorithms.
❤4
Forwarded from Data Science Jupyter Notebooks
Python library RetinaFace for face detection and working with key points (eyes, nose, mouth)
Supports face alignment, easily installed via
An excellent tool for tasks in computer vision and face recognition.
Usage examples:
👉 @DataScienceN
Supports face alignment, easily installed via
pip install retina-face
, and works based on deep models from the insightface project.An excellent tool for tasks in computer vision and face recognition.
Usage examples:
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img1.jpg")
print(resp)
{
"face_1": {
"score": 0.9993440508842468,
"facial_area": [155, 81, 434, 443],
"landmarks": {
"right_eye": [257.82974, 209.64787],
"left_eye": [374.93427, 251.78687],
"nose": [303.4773, 299.91144],
"mouth_right": [228.37329, 338.73193],
"mouth_left": [320.21982, 374.58798]
}
}
}
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Forwarded from Data Science Machine Learning Data Analysis
📌 How to Build a Genetic Algorithm from Scratch in Python
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read
A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read
A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…
💯3
Forwarded from Data Science Machine Learning Data Analysis
📌 Extracting Structured Vehicle Data from Images
🗂 Category:
🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read
Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,…
🗂 Category:
🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read
Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,…
❤3
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Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
👉 @DataScienceM
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
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Great find for developers: free cheat sheets on Deep Learning and PyTorch
A detailed guide to creating and training neural networks - link
Basic principles and practice of working with PyTorch - link
👉 @CODEPROGRAMMER
A detailed guide to creating and training neural networks - link
Basic principles and practice of working with PyTorch - link
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800+ SQL Server Interview Questions and Answers .pdf
1 MB
It also includes tasks for self-study and many examples.
The collection is perfect for those who want to improve their SQL skills, refresh their knowledge, and test themselves.
https://t.iss.one/addlist/8_rRW2scgfRhOTc0
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Forwarded from Data Science Machine Learning Data Analysis
📌 Missing Value Imputation, Explained: A Visual Guide with Code Examples for Beginners
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-27 | ⏱️ Read time: 13 min read
One (tiny) dataset, six imputation methods?
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-27 | ⏱️ Read time: 13 min read
One (tiny) dataset, six imputation methods?
❤8
Python Cheat Sheet (very very important)
📖 Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.
Link: https://discord.com/channels/942740928706281524/1423994784720359567/1424711790947864669
📖 Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.
Link: https://discord.com/channels/942740928706281524/1423994784720359567/1424711790947864669
❤3
Forwarded from Python | Machine Learning | Coding | R
“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.
✅ Videos:
Everything here ⇒ https://lnkd.in/ePfB8_rk
➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn
✅ Courses:
All Courses here ⇒ https://lnkd.in/eKKs9ves
➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9
✅ Guides:
Access all ⇒ https://lnkd.in/e-GA-HRh
➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh
✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX
✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE
Access all ⇒ https://lnkd.in/e-GA-HRh
By: https://t.iss.one/CodeProgrammer🟡
Here’s the real path, the courses, papers and repos that matter.
Everything here ⇒ https://lnkd.in/ePfB8_rk
All Courses here ⇒ https://lnkd.in/eKKs9ves
Access all ⇒ https://lnkd.in/e-GA-HRh
Access all ⇒ https://lnkd.in/e-GA-HRh
By: https://t.iss.one/CodeProgrammer
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👨🏻💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.
📝 The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image.
✅ Works with PDFs, images, and website links.
☑️ Can chunk and process very large documents (up to 1000 pages) by itself.
✔️ Outputs both JSON and Markdown formats.
☑️ Even specifies the exact location of each section on the page.
✔️ Supports parallel and batch processing.
┌🥵 Agentic Document Extraction
├🌎 Website
└🐱 GitHub Repos
🌐 #DataScience #DataScience
➖➖➖➖➖➖➖➖➖➖➖➖➖
https://t.iss.one/CodeProgrammer
pip install agentic-doc
┌
├
└
➖➖➖➖➖➖➖➖➖➖➖➖➖
https://t.iss.one/CodeProgrammer
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👨🏻💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.
➖➖➖➖➖➖➖➖➖➖➖➖➖
https://t.iss.one/CodeProgrammer
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Forwarded from Thor data
🚀 Thordata Proxy: Bypass Anti-Scraping for Data Projects
Facing these issues in data collection?
🔴 IP blocks interrupting workflows
🟡 CAPTCHAs breaking automation
🟢 Geo-restrictions limiting data access
Thordata Proxy provides high-performance proxy solutions for ML/DS professionals:
🔥 Key Features
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📊 Perfect For:
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20% off with code: IsyGLO5o
Official Channel : https://t.iss.one/thordataproxy
Facing these issues in data collection?
🔴 IP blocks interrupting workflows
🟡 CAPTCHAs breaking automation
🟢 Geo-restrictions limiting data access
Thordata Proxy provides high-performance proxy solutions for ML/DS professionals:
🔥 Key Features
Seamless Integration: Native support for Python (Requests/Scrapy/Selenium), R, Spark
Global Coverage: 200+ countries with city-level targeting
Anti-Blocking: Residential/ISP proxies mimic real users
Low Latency: <0.8s average response time, 99.9% uptime
Compliant: GDPR/CCPA compliant for public data only
📊 Perfect For:
Training data collection for ML models/Competitive pricing monitoring/Cross-region social media analysis/Ad verification testing
🌟 Community Offer
🔗 Start now: https://www.thordata.com/?ls=DhthVzyG&lk=Data
20% off with code: IsyGLO5o
Official Channel : https://t.iss.one/thordataproxy
Thordata
Thordata - High-Quality Proxy Service for Web Data Scraping
Thordata's precision proxy solution was chosen to ensure seamless data collection. Enjoy the best prices and services tailored to your needs.
❤4
Forwarded from Python Data Science Jobs & Interviews
1. What is the output of the following code?
2. Which of the following data types is immutable in Python?
A) List
B) Dictionary
C) Set
D) Tuple
3. Write a Python program to reverse a string without using built-in functions.
4. What will be printed by this code?
5. Explain the difference between
6. How do you handle exceptions in Python? Provide an example.
7. What is the output of:
8. Which keyword is used to define a function in Python?
A) def
B) function
C) func
D) define
9. Write a program to find the factorial of a number using recursion.
10. What does the
11. What will be the output of:
12. Explain the concept of list comprehension with an example.
13. What is the purpose of the
14. Write a program to check if a given string is a palindrome.
15. What is the output of:
16. Describe how Python manages memory (garbage collection).
17. What will be printed by:
18. Write a Python program to generate the first n Fibonacci numbers.
19. What is the difference between
20. What is the use of the
#PythonQuiz #CodingTest #ProgrammingExam #MultipleChoice #CodeOutput #PythonBasics #InterviewPrep #CodingChallenge #BeginnerPython #TechAssessment #PythonQuestions #SkillCheck #ProgrammingSkills #CodePractice #PythonLearning #MCQ #ShortAnswer #TechnicalTest #PythonSyntax #Algorithm #DataStructures #PythonProgramming
By: @DataScienceQ 🚀
x = [1, 2, 3]
y = x
y.append(4)
print(x)
2. Which of the following data types is immutable in Python?
A) List
B) Dictionary
C) Set
D) Tuple
3. Write a Python program to reverse a string without using built-in functions.
4. What will be printed by this code?
def func(a, b=[]):
b.append(a)
return b
print(func(1))
print(func(2))
5. Explain the difference between
==
and is
operators in Python.6. How do you handle exceptions in Python? Provide an example.
7. What is the output of:
print(2 ** 3 ** 2)
8. Which keyword is used to define a function in Python?
A) def
B) function
C) func
D) define
9. Write a program to find the factorial of a number using recursion.
10. What does the
*args
parameter do in a function?11. What will be the output of:
list1 = [1, 2, 3]
list2 = list1.copy()
list2[0] = 10
print(list1)
12. Explain the concept of list comprehension with an example.
13. What is the purpose of the
__init__
method in a Python class?14. Write a program to check if a given string is a palindrome.
15. What is the output of:
a = [1, 2, 3]
b = a[:]
b[0] = 10
print(a)
16. Describe how Python manages memory (garbage collection).
17. What will be printed by:
x = "hello"
y = "world"
print(x + y)
18. Write a Python program to generate the first n Fibonacci numbers.
19. What is the difference between
range()
and xrange()
in Python 2?20. What is the use of the
lambda
function in Python? Give an example. #PythonQuiz #CodingTest #ProgrammingExam #MultipleChoice #CodeOutput #PythonBasics #InterviewPrep #CodingChallenge #BeginnerPython #TechAssessment #PythonQuestions #SkillCheck #ProgrammingSkills #CodePractice #PythonLearning #MCQ #ShortAnswer #TechnicalTest #PythonSyntax #Algorithm #DataStructures #PythonProgramming
By: @DataScienceQ 🚀
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