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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
Alpha $SOL Team launching today at 20:00 UTC, make sure you’re not fading!
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📌 Your First 90 Days as a Data Scientist
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-14 | ⏱️ Read time: 8 min read
A practical onboarding checklist for building trust, business fluency, and data intuition
#DataScience #AI #Python
These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth
Lecture 1 - Transformer: https://lnkd.in/dGnQW39t
Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH
Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP
Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN
Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB
Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM
Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm
Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb
Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN
Start understanding #LLMs in depth from the experts. Go through each step-by-step video.
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مبادرة جميلة يرجى الانضمام اليها - للسوريين (مبادرة هامة) 🇸🇾
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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✅ /channel/Codeprogrammer
#KMeans clustering animation in the style of 3blue1brown
👉 @CODEPROGRAMMER
NumPy Cheat Sheet: Data Analysis in Python
This #Python cheat sheet is a quick reference for #NumPy beginners.
Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python
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👉 @codeprogrammer
Trackers v2.1.0 has been released. In this release, support for ByteTrack has been added - a fast tracking-by-detection algorithm that maintains stable IDs even during occlusions.
Link: https://github.com/roboflow/trackers
pip install trackers
🅰 Админотека — если у тебя есть тгк и ты тоже не хочешь ходить на работу
Читать полностью…
💛 Top 10 Best Websites to Learn Machine Learning ⭐️
by [@codeprogrammer]
---
🧠 Google’s ML Course
🔗 https://developers.google.com/machine-learning/crash-course
📈 Kaggle Courses
🔗 https://kaggle.com/learn
🧑🎓 Coursera – Andrew Ng’s ML Course
🔗 https://coursera.org/learn/machine-learning
⚡️ Fast.ai
🔗 https://fast.ai
🔧 Scikit-Learn Documentation
🔗 https://scikit-learn.org
📹 TensorFlow Tutorials
🔗 https://tensorflow.org/tutorials
🔥 PyTorch Tutorials
🔗 https://docs.pytorch.org/tutorials/
🏛️ MIT OpenCourseWare – Machine Learning
🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/
✍️ Towards Data Science (Blog)
🔗 https://towardsdatascience.com
---
💡 Which one are you starting with? Drop a comment below! 👇
#MachineLearning #LearnML #DataScience #AI
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Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment.
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👍 A fresh deep learning course from MIT is now available publicly
A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
tags: #python #deeplearning
➡ @codeprogrammer
Design patterns are proven solutions to common problems in development. If you've ever found yourself constantly writing the same thing when creating objects or struggling with managing different types of objects, then the factory pattern might be exactly what you need.
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
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teach Transformer.
Register Free:
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🐱 5 of the Best GitHub Repos
🔃 for Data Scientists
👨🏻💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.
▶️ But doing projects was like water on fire and helped me a lot to build my skills.
〰 Repo Awesome Data Analysis
🏷 A complete treasure trove of everything you need to start: SQL, Python, AI, data analysis, and more... In short, if you want to start from zero and strengthen your foundation, start here first.
➖ ➖ ➖
〰 Repo Data Scientist Handbook
🏷 A concise handbook that tells you what you need to learn and what you can ignore for now.
➖ ➖ ➖
〰 Repo Cookiecutter Data Science
🏷 A standard project template used by professionals. With this template, you can structure your data analysis and AI projects like a pro.
➖ ➖ ➖
〰 Repo Data Science Cookie Cutter
🏷 This is also a very clean project template that teaches you how to build a data project that won’t fall apart tomorrow and can be easily updated. Meaning your projects will be useful in the real world from the start.
➖ ➖ ➖
〰 Repo ML From Scratch
🏷 Here, the main AI algorithms are implemented from scratch in simple language. It’s great for understanding how models really work and for explaining them well in your interviews.
🌐 #Data_Science #DataScience
Neural Networks: How They Learn and Predict
/channel/CodeProgrammer
9 key concepts of artificial intelligence, explained in 7 minutes
- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA
👉 @Python53
AI Developers — finally something serious.
A German company 🇩🇪 (Brainlancer GmbH) is launching a curated B2B platform on April 1st, 2026.
Not a freelance marketplace.
Not an agency network.
A verified AI builder network.
Only a few spots are still open.
If you can actually ship outcomes like:
• RAG / Agents in production
• Automations + API integrations
• FastAPI tools, internal apps, backend systems
→ apply now (free + anonymous).
http://assesment.brainlancer.com/?src=telegram
Step 1: 5 min form
Step 2: 15–20 min AI interview
Step 3: short call → early access
👉 Brainlancer.com (Landingpage)
👉 https://www.linkedin.com/in/soner-catakli/ (CEO)
nature papers: 1200$
Q1 and Q2 papers 700$
Q3 and Q4 papers 400$
Doctoral thesis (complete) 600$
M.S thesis 300$
paper simulation 200$
Contact @Omidyzd62
🧠 Converting images to ASCII: text instead of pixels
Want to turn any image into ASCII art? It's not magic, just simple brightness processing.
It's tedious and stupid to do it manuallyimg = [
[255, 0, 0],
[0, 255, 0]
]
# Now we need to pick a symbol for each pixel...
# What a hassle.
Problem:
Manually selecting symbols by brightness is a pain. We need to automate the conversion of grayscale to symbols.
✔️ The right way (using gradation)
from PIL import Image
def image_to_ascii(path, width=100):
img = Image.open(path)
aspect = img.height / img.width
height = int(width * aspect * 0.55)
img = img.resize((width, height)).convert('L')
ascii_chars = '@%#*+=-:. '
pixels = img.getdata()
ascii_art = '\n'.join(
ascii_chars[pixel * (len(ascii_chars) - 1) // 255]
for pixel in pixels
)
lines = [ascii_art[i:i+width] for i in range(0, len(ascii_art), width)]
return '\n'.join(lines)
print(image_to_ascii('cat.jpg'))
class AsciiConverter:
PALETTES = {
'default': '@%#*+=-:. ',
'blocks': '█rayed ',
'detailed': '$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,"^`\'. '
}
def __init__(self, palette_name='default'):
if palette_name not in self.PALETTES:
raise ValueError(f'Нет такой палитры, идиот. Выбери из: {list(self.PALETTES.keys())}')
self.chars = self.PALETTES[palette_name]
def convert(self, image_path, width=80):
# ... code to convert using self.chars ...
return ascii_result
🔵Width - determines the size of the final ASCII art
🔵Character palette - affects the detail and style
🔵Aspect ratio - important for correct display
🔵Inversion - you can invert the brightness for a dark background
Here's the full path I would recommend to build production-grade AI agents this year:
▪️a foundation in Python and algorithms
▪️mathematics and the basics of ML
▪️transformers and LLMs
▪️prompt engineering
▪️memory and RAG
▪️tools and integrations
▪️frameworks like LangChain or CrewAI
▪️multi-agent systems
▪️testing, deployment, and security
👉 @Codeprogrammer
GitHub has launched its learning platform: all #courses and certificates in one place.
#Git, #GitHub, #MCP, using #AI, #VSCode, and much more.
And most of the content is #free: → https://learn.github.com
👉 @codeprogrammer
ML engineers, take note: structured ML reference guide
Link: https://ml-cheatsheet.readthedocs.io/en/latest/
There are no courses, no redundant theory, and no lengthy lectures here, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base.
👉 @codeprogrammer
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Data Science Interview questions
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
/channel/CodeProgrammer
These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯
Repo: https://udlbook.github.io/udlbook/
👉 @codeprogrammer